scholarly journals Background Modeling with Extracted Dynamic Pixels for Pumping Unit Surveillance

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Tianming Yu ◽  
Jianhua Yang ◽  
Wei Lu
Keyword(s):  
State Of The Art ◽  
Background Modeling ◽  
Object Motion ◽  
Dynamic Scenes ◽  
Complex Dynamic ◽  
Dynamic Background ◽  
Sample Distribution ◽  
Modeling Methods ◽  
Monitoring Applications ◽  
Pumping Unit

Background modeling plays an important role in the application of intelligent video surveillance. Researchers have presented diverse approaches to support the development of dynamic background modeling. However, in the case of pumping unit surveillance, traditional background modeling methods often mistakenly detect the periodic rotational pumping unit as the foreground object. To address this problem here, we propose a novel background modeling method for foreground segmentation, particularly in dynamic scenes that include a rotational pumping unit. In the proposed method, the ViBe method is employed to extract possible foreground pixels from the sequence frames and then segment the video image into dynamic and static regions. Subsequently, the kernel density estimation (KDE) method is used to build a background model with dynamic samples of each pixel. The bandwidth and threshold of the KDE model are calculated according to the sample distribution and extremum of each dynamic pixel. In addition, the strategy of sample adjustment combines regular and real-time updates. The performance of the proposed method is evaluated against several state-of-the-art methods applied to complex dynamic scenes consisting of a rotational pumping unit. Experimental results show that the proposed method is available for periodic object motion scenario monitoring applications.

scholarly journals WePBAS: A Weighted Pixel-Based Adaptive Segmenter for Change Detection

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2672
Author(s):  
Wenhui Li ◽  
Jianqi Zhang ◽  
Ying Wang
Keyword(s):  
Change Detection ◽  
High Weight ◽  
State Of The Art ◽  
Diffusion Mechanism ◽  
Background Modeling ◽  
Background Model ◽  
Dynamic Background ◽  
Low Weight ◽  
Model Update ◽  
Sample Set

The pixel-based adaptive segmenter (PBAS) is a classic background modeling algorithm for change detection. However, it is difficult for the PBAS method to detect foreground targets in dynamic background regions. To solve this problem, based on PBAS, a weighted pixel-based adaptive segmenter named WePBAS for change detection is proposed in this paper. WePBAS uses weighted background samples as a background model. In the PBAS method, the samples in the background model are not weighted. In the weighted background sample set, the low-weight background samples typically represent the wrong background pixels and need to be replaced. Conversely, high-weight background samples need to be preserved. According to this principle, a directional background model update mechanism is proposed to improve the segmentation performance of the foreground targets in the dynamic background regions. In addition, due to the “background diffusion” mechanism, the PBAS method often identifies small intermittent motion foreground targets as background. To solve this problem, an adaptive foreground counter was added to the WePBAS to limit the “background diffusion” mechanism. The adaptive foreground counter can automatically adjust its own parameters based on videos’ characteristics. The experiments showed that the proposed method is competitive with the state-of-the-art background modeling method for change detection.

scholarly journals Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

2021 ◽  
Vol 11 (5) ◽  
pp. 603
Author(s):  
Chunlei Shi ◽  
Xianwei Xin ◽  
Jiacai Zhang
Keyword(s):  
Machine Learning ◽  
Domain Adaptation ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Target Domain ◽  
Sample Distribution ◽  
Machine Learning Methods ◽  
First Time

Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models. Herein, the three-way decision theory was introduced into unsupervised domain adaptation in the first time, and applied to optimize the pseudolabel of the target domain/site from functional magnetic resonance imaging (fMRI) features related to ASD patients. The experimental results using multisite fMRI data show that our method not only narrows the gap of the sample distribution among domains but is also superior to the state-of-the-art domain adaptation methods in ASD recognition. Specifically, the ASD recognition accuracy of the proposed method is improved on all the six tasks, by 70.80%, 75.41%, 69.91%, 72.13%, 71.01% and 68.85%, respectively, compared with the existing methods.

Problem of angular motion control of a non-cooperative on-orbit service object

2021 ◽  
Vol 2021 (1) ◽  
pp. 37-50
Author(s):  
A.A. Fokov ◽  
◽  
O.P. Savchuk ◽  
Keyword(s):  
Motion Control ◽  
State Of The Art ◽  
Object Motion ◽  
The State ◽  
Angular Motion ◽  
Motion Parameter ◽  
Rotary Motion ◽  
Parameter Determination ◽  
Service Object

The realization of existing projects of on-orbit servicing and the development of new ones is a steady trend in the development of space technology. In many cases, on-orbit service clients are objects that exhibit an undesired rotary motion, which renders their servicing difficult or impossible. The problem of on-orbit service object motion control determines the topicality of studies aimed not only at the refinement of methods and algorithms of controlling both the translational and the rotary motion of an object, but also at the development and refinement of methods of onboard determination of the object – service spacecraft relative motion parameters. This paper overviews the state of the art of the problem of object motion parameter determination in on-orbit servicing tasks and existing methods of object motion control and angular motion damping and specifies lines of further investigations into the angular motion control of non-cooperative service objects. Based on the analysis of publications on the subject, the applicability of onboard means for object motion parameter determination is characterized. The analysis of the applicability of methods of remote determination of the parameters of an unknown non-cooperative object from a service spacecraft shows that they are at the research stage. The input data for the verification of methods proposed in the literature were simulated or taken from ground experiments or previous missions. Contact and contactless methods of angular motion control of non-cooperative on-orbit service objects are considered. From the state of the art of investigations into the contactless motion control of on-orbit service objects it may be concluded that the most advanced contactless method of motion control of an on-orbit service object is a technology based on the use of an ion beam directed to the object from an electrojet engine onboard a service spacecraft. Lines of further investigations into non-cooperative object motion control are proposed.

PharmKG: a dedicated knowledge graph benchmark for bomedical data mining

2020 ◽  
Author(s):  
Shuangjia Zheng ◽  
Jiahua Rao ◽  
Ying Song ◽  
Jixian Zhang ◽  
Xianglu Xiao ◽  
...  
Keyword(s):  
Chemical Structure ◽  
State Of The Art ◽  
Critical Role ◽  
Global Network ◽  
Evaluation Metrics ◽  
Complex Nature ◽  
Specific Information ◽  
Biomedical Knowledge ◽  
Domain Specific ◽  
Modeling Methods

Abstract Biomedical knowledge graphs (KGs), which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods and non-uniform evaluation metrics. In this work, we established a comprehensive KG system for the biomedical field in an attempt to bridge the gap. Here, we introduced PharmKG, a multi-relational, attributed biomedical KG, composed of more than 500 000 individual interconnections between genes, drugs and diseases, with 29 relation types over a vocabulary of ~8000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure and disease word embedding, while preserving the semantic and biomedical features. For baselines, we offered nine state-of-the-art KG embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a KG in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical KG construction, embedding and application.

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