scholarly journals Detection of Unusual Targets in Traffic Images Based on One-Class Extreme Machine Learning

2020 ◽  
Vol 37 (6) ◽  
pp. 1003-1008
Author(s):  
Lei Yu ◽  
Binglin Zhang ◽  
Rui Li
Keyword(s):  
Extreme Learning Machine ◽  
Target Detection ◽  
Supervised Classification ◽  
Detection Methods ◽  
Resource Saving ◽  
Multi Level ◽  
Learning Machine ◽  
Hypergraph Laplacian ◽  
The Cost ◽  
Feature Expression

In traffic image target detection, unusual targets like a running dog has not been paid sufficient attention. The mature detection methods for general targets cannot be directly applied to detect unusual targets, owing to their high complexity, poor feature expression ability, and requirement for numerous manual labels. To effectively detect unusual targets in traffic images, this paper proposes a multi-level semi-supervised one-class extreme learning machine (ML-S2OCELM). Specifically, the extreme learning machine (ELM) was chosen as the basis to develop a classifier, whose variables could be calculated directly at the cost of limited computing resources. The hypergraph Laplacian array was employed to improve the depiction of data smoothness, making semi-supervised classification more accurate. Furthermore, a stack auto-encoder (AE) was introduced to implement a multi-level neural network (NN), which can extract discriminative eigenvectors with suitable dimensions. Experiments show that the proposed method can efficiently screen out traffic images with unusual targets with only a few positive labels. The research results provide a time-efficient, and resource-saving instrument for feature expression and target detection.

scholarly journals Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

2021 ◽  
Vol 11 (9) ◽  
pp. 3867
Author(s):  
Zhewei Liu ◽  
Zijia Zhang ◽  
Yaoming Cai ◽  
Yilin Miao ◽  
Zhikun Chen
Keyword(s):  
Extreme Learning Machine ◽  
Supervised Classification ◽  
Structured Data ◽  
Model Complex ◽  
Euclidean Domain ◽  
Noise Data ◽  
Data Points ◽  
Learning Machine ◽  
Random Hypergraph ◽  
Special Case

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.

scholarly journals Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

2018 ◽  
Vol 25 (4) ◽  
pp. 43 ◽  
Author(s):  
Camila Martins Saporetti ◽  
Grasiele Regina Duarte ◽  
Tales Lima Fonseca ◽  
Leonardo Goliatt Da Fonseca ◽  
Egberto Pereira
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Reservoir Characterization ◽  
Differential Evolution Algorithm ◽  
Activation Function ◽  
Development Planning ◽  
Lithology Identification ◽  
Learning Machine ◽  
The Cost

Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an  activation function recently proposed in the literature is implemented and tested. The  computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning.

scholarly journals Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM)

2021 ◽  
Vol 13 (23) ◽  
pp. 4918
Author(s):  
Te Han ◽  
Yuqi Tang ◽  
Xin Yang ◽  
Zefeng Lin ◽  
Bin Zou ◽  
...  
Keyword(s):  
Change Detection ◽  
Extreme Learning Machine ◽  
State Of The Art ◽  
Sample Selection ◽  
Training Sample ◽  
Google Earth ◽  
Detection Methods ◽  
Feature Maps ◽  
Optical Images ◽  
Learning Machine

To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method.

scholarly journals Multi-Agent System Based on the Extreme Learning Machine and Fuzzy Control for Intelligent Energy Management in Microgrid

2018 ◽  
Vol 29 (1) ◽  
pp. 877-893 ◽  
Author(s):  
Dounia El Bourakadi ◽  
Ali Yahyaouy ◽  
Jaouad Boumhidi
Keyword(s):  
Extreme Learning Machine ◽  
Electricity Market ◽  
Weather Data ◽  
Multi Agent System ◽  
Power Prediction ◽  
Agent System ◽  
On Demand ◽  
Multi Agent ◽  
Learning Machine ◽  
The Cost

Abstract Renewable energies constitute an alternative to fossil energies for several reasons. The microgrid can be assumed as the ideal way to integrate a renewable energy source in the production of electricity and give the consumer the opportunity to participate in the electricity market not just like a consumer but also like a producer. In this paper, we present a multi-agent system based on wind and photovoltaic power prediction using the extreme learning machine algorithm. This algorithm was tested on real weather data taken from the region of Tetouan City in Morocco. The process aimed to implement a microgrid located in Tetouan City and composed of different generation units (solar and wind energies were combined together to increase the efficiency of the system) and storage units (batteries were used to ensure the availability of power on demand as much as possible). In the proposed architecture, the microgrid can exchange electricity with the main grid; therefore, it can buy or sell electricity. Thus, the goal of our multi-agent system is to control the amount of power delivered or taken from the main grid in order to reduce the cost and maximize the benefit. To address uncertainties in the system, we use fuzzy logic control to manage the flow of energy, to ensure the availability of power on demand, and to make a reasonable decision about storing or selling electricity.

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