scholarly journals Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring

2021 ◽  
Vol 11 (2) ◽  
pp. 533
Author(s):  
Barbara Cannas ◽  
Sara Carcangiu ◽  
Daniele Carta ◽  
Alessandra Fanni ◽  
Carlo Muscas
Keyword(s):  
Classification Problem ◽  
Classification Performance ◽  
Energy Meter ◽  
Large Spread ◽  
Time Signals ◽  
Load Monitoring ◽  
Good Classification Performance ◽  
Nonlinear Devices ◽  
Electric Loads ◽  
Optimal Set

Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Signal Classification Algorithms over Time Selective Channels

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1714
Author(s):  
Mohamed Marey ◽  
Hala Mostafa
Keyword(s):  
Block Code ◽  
Classification Problem ◽  
Classification Performance ◽  
Signal Classification ◽  
Final Decision ◽  
Time Block ◽  
Channel Response ◽  
Over Time ◽  
Parallel Fashion

In this work, we propose a general framework to design a signal classification algorithm over time selective channels for wireless communications applications. We derive an upper bound on the maximum number of observation samples over which the channel response is an essential invariant. The proposed framework relies on dividing the received signal into blocks, and each of them has a length less than the mentioned bound. Then, these blocks are fed into a number of classifiers in a parallel fashion. A final decision is made through a well-designed combiner and detector. As a case study, we employ the proposed framework on a space-time block-code classification problem by developing two combiners and detectors. Monte Carlo simulations show that the proposed framework is capable of achieving excellent classification performance over time selective channels compared to the conventional algorithms.

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

scholarly journals Facial Expression Analysis by Machine Learning

2010 ◽  
pp. 239-258
Author(s):  
Siu-Yeung Cho ◽  
Teik-Toe Teoh ◽  
Yok-Yen Nguwi
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Classification Performance ◽  
Difficult Problem ◽  
Facial Features ◽  
Expression Recognition ◽  
Real Time Processing ◽  
Good Classification Performance

Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

scholarly journals Efficient pan-cancer whole-slide image classification and outlier detection using convolutional neural networks

2019 ◽  
Author(s):  
Seda Bilaloglu ◽  
Joyce Wu ◽  
Eduardo Fierro ◽  
Raul Delgado Sanchez ◽  
Paolo Santiago Ocampo ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Classification Problem ◽  
Classification Performance ◽  
Neoplastic Tissue ◽  
Multiple Tumor ◽  
Slide Image ◽  
Prediction Systems ◽  
Multi Class Classification ◽  
The Many ◽  
Whole Slide Images

AbstractVisual analysis of solid tissue mounted on glass slides is currently the primary method used by pathologists for determining the stage, type and subtypes of cancer. Although whole slide images are usually large (10s to 100s thousands pixels wide), an exhaustive though time-consuming assessment is necessary to reduce the risk of misdiagnosis. In an effort to address the many diagnostic challenges faced by trained experts, recent research has been focused on developing automatic prediction systems for this multi-class classification problem. Typically, complex convolutional neural network (CNN) architectures, such as Google’s Inception, are used to tackle this problem. Here, we introduce a greatly simplified CNN architecture, PathCNN, which allows for more efficient use of computational resources and better classification performance. Using this improved architecture, we trained simultaneously on whole-slide images from multiple tumor sites and corresponding non-neoplastic tissue. Dimensionality reduction analysis of the weights of the last layer of the network capture groups of images that faithfully represent the different types of cancer, highlighting at the same time differences in staining and capturing outliers, artifacts and misclassification errors. Our code is available online at: https://github.com/sedab/PathCNN.

scholarly journals Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Jihyun Kim ◽  
Thi-Thu-Huong Le ◽  
Howon Kim
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Distribution System ◽  
Short Term Memory ◽  
Electrical Power ◽  
Electricity Consumption ◽  
Classification Performance ◽  
Robust Solution ◽  
Proposed Model ◽  
Load Monitoring

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

Artificial Odour Classification System

2018 ◽  
pp. 25-37
Author(s):  
Nor Idayu Mahat ◽  
Maz Jamilah Masnan ◽  
Ali Yeon Md Shakaff ◽  
Ammar Zakaria ◽  
Muhd Khairulzaman Abdul Kadir
Keyword(s):  
Feature Selection ◽  
Mahalanobis Distance ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Good Choice ◽  
Classification Rule ◽  
Good Classification Performance ◽  
The Right ◽  
The Cost

This chapter overviews the issue of multicollinearity in electronic nose (e-nose) classification and investigates some analytical solutions to deal with the problem. Multicollinearity effect may harm classification analysis from producing good parameters estimate during the construction of the classification rule. The common approach to deal with multicollinearity is feature extraction. However, the criterion used in extracting the raw features based on variances may not be appropriate for the ultimate goal of classification accuracy. Alternatively, feature selection method would be advisable as it chooses only valuable features. Two distance-based criteria in determining the right features for classification purposes, Wilk's Lambda and bounded Mahalanobis distance, are applied. Classification with features determined by bounded Mahalanobis distance statistically performs better than Wilk's Lambda. This chapter suggests that classification of e-nose with feature selection is a good choice to limit the cost of experiments and maintain good classification performance.

Semi-supervised learning using autodidactic interpolation on sparse representation-based multiple one-dimensional embedding

2019 ◽  
Vol 17 (03) ◽  
pp. 1950013 ◽  
Author(s):  
Hao Deng ◽  
Chao Ma ◽  
Lijun Shen ◽  
Chuanwu Yang
Keyword(s):  
Sparse Representation ◽  
Euclidean Distance ◽  
Main Idea ◽  
Classification Problem ◽  
Classification Performance ◽  
One Dimensional ◽  
Adaptive Interpolation ◽  
Shortest Distance ◽  
The Common ◽  
Sample Set

In this paper, we present a novel semi-supervised classification method based on sparse representation (SR) and multiple one-dimensional embedding-based adaptive interpolation (M1DEI). The main idea of M1DEI is to embed the data into multiple one-dimensional (1D) manifolds satisfying that the connected samples have shortest distance. In this way, the problem of high-dimensional data classification is transformed into a 1D classification problem. By alternating interpolation and averaging on the multiple 1D manifolds, the labeled sample set of the data can enlarge gradually. Obviously, proper metric facilitates more accurate embedding and further helps improve the classification performance. We develop a SR-based metric, which measures the affinity between samples more accurately than the common Euclidean distance. The experimental results on several databases show the effectiveness of the improvement.

scholarly journals EBOC: Ensemble-Based Ordinal Classification in Transportation

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Pelin Yıldırım ◽  
Ulaş K. Birant ◽  
Derya Birant
Keyword(s):  
Historical Data ◽  
Classification Problem ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Ordinal Classification ◽  
Adaboost Algorithm ◽  
Education And Health ◽  
Transportation Sector ◽  
C4.5 Decision Tree ◽  
Target Attribute

Learning the latent patterns of historical data in an efficient way to model the behaviour of a system is a major need for making right decisions. For this purpose, machine learning solution has already begun its promising marks in transportation as well as in many areas such as marketing, finance, education, and health. However, many classification algorithms in the literature assume that the target attribute values in the datasets are unordered, so they lose inherent order between the class values. To overcome the problem, this study proposes a novel ensemble-based ordinal classification (EBOC) approach which suggests bagging and boosting (AdaBoost algorithm) methods as a solution for ordinal classification problem in transportation sector. This article also compares the proposed EBOC approach with ordinal class classifier and traditional tree-based classification algorithms (i.e., C4.5 decision tree, RandomTree, and REPTree) in terms of accuracy. The results indicate that the proposed EBOC approach achieves better classification performance than the conventional solutions.

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