A Study on Multi-Data Source Fusion Method for Petroleum Pipeline Leak Detection

2010 ◽  
Author(s):  
Wei Liang ◽  
Laibin Zhang
Keyword(s):  
Supervisory Control ◽  
Integrated Approach ◽  
Leak Detection ◽  
Data Sources ◽  
Fusion Method ◽  
Challenging Problem ◽  
New Approach ◽  
Multiple Data ◽  
Data Source

This paper describes a new approach of data source fusion based on process fusion entropy for leak detecting of product pipelines. Data sources are either single-channeled or multi-channeled: single-channeled data sources can be structured or semi-structured process steady entropy, whereas multi-channeled sources are singular spectrum entropy and power spectrum entropy. In order to develop data sources fusion systems for pipeline leak detection in real-time contexts, we need to study all issues raised by the matching paradigms. This challenging problem becomes crucial with the dominating role of the internet. Classical approaches of data integration, based on schemas mediation, are not suitable to the pipeline SCADA (Supervisory Control and Data Acquisition) environment where data is frequently modified or updated. Therefore, we develop a loosely integrated approach that takes into consideration both steady and transient states which must be separated correctly in order to integrate new data sources. Moreover, we introduce a process fusion entropy-based Multi-data source Fusion Method (MFM) that aims to define and retrieve conflicting data from multiple data sources.

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

scholarly journals 3D Geometry-Based Indoor Network Extraction for Navigation Applications Using SFCGAL

2020 ◽  
Vol 9 (7) ◽  
pp. 417 ◽  
Author(s):  
Jernej Tekavec ◽  
Anka Lisec
Keyword(s):  
Integrated Approach ◽  
Indoor Navigation ◽  
Data Sources ◽  
Network Analyses ◽  
Multiple Data ◽  
3D Cadastre ◽  
3D Geometry ◽  
Building Information ◽  
And Performance ◽  
Simple Features

This study is focused on indoor navigation network extraction for navigation applications based on available 3D building data and using SFCGAL library, e.g. simple features computational geometry algorithms library. In this study, special attention is given to 3D cadastre and BIM (building information modelling) datasets, which have been used as data sources for 3D geometric indoor modelling. SFCGAL 3D functions are used for the extraction of an indoor network, which has been modelled in the form of indoor connectivity graphs based on 3D geometries of indoor features. The extraction is performed by the integration of extract transform load (ETL) software and the spatial database to support multiple data sources and provide access to SFCGAL functions. With this integrated approach, the current lack of straightforward software support for complex 3D spatial analyses is addressed. Based on the developed methodology, we perform and discuss the extraction of an indoor navigation network from 3D cadastral and BIM data. The efficiency and performance of the network analyses were evaluated using the processing and query execution times. The results show that the proposed methodology for geometry-based navigation network extraction of buildings is efficient and can be used with various types of 3D geometric indoor data.

Dependency Injection mechanism in Intelligence Information System of Power Quality

2012 ◽  
Vol 241-244 ◽  
pp. 3085-3091
Author(s):  
Jian Gong ◽  
Cui Hong Lv ◽  
Lin Hai Qi ◽  
Su Xia Ma
Keyword(s):  
Information System ◽  
Power Quality ◽  
Data Sources ◽  
Monitoring Data ◽  
Multiple Data Source ◽  
Data Provider ◽  
Multiple Data ◽  
Injection Mechanism ◽  
Data Source ◽  
Intelligent Information

The calculation subsystems of the power quality intelligent information system will face many types of monitoring data source, and when different data sources provide data for calculation subsystem, it does not need to change algorithm but need to change the way how to get the data needed; then how to make the calculation subsystem does not alter with the change of data provider becomes a necessary demand;Aiming at the problem this paper put forward a set of solutions, which are dependent on dependency-injection, to help the calculation subsystem in multiple data source supporting.

scholarly journals Learning Generative Adversarial Networks from Multiple Data Sources

2019 ◽  
Author(s):  
Trung Le ◽  
Quan Hoang ◽  
Hung Vu ◽  
Tu Dinh Nguyen ◽  
Hung Bui ◽  
...  
Keyword(s):  
Generative Models ◽  
Data Sources ◽  
Black Hair ◽  
Primary Data ◽  
Generative Adversarial Networks ◽  
Auxiliary Data ◽  
Adversarial Networks ◽  
Multiple Data ◽  
Data Source ◽  
Push And Pull

Generative Adversarial Networks (GANs) are a powerful class of deep generative models. In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources. For this problem, we enrich both GANs' formulations and applications by introducing pushing forces that thrust generated samples away from given auxiliary data sources. We term our method Push-and-Pull GAN (P2GAN). We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN's effectiveness in addressing the mode collapsing problem. The results show that P2GAN outperforms the state-of-the-art baselines. For the problem of generating data with constraints, we show that P2GAN can successfully avoid generating specific features such as black hair.

scholarly journals Implementation of micro application storage with high reliability based on Oracle 12c

2019 ◽  
Vol 272 ◽  
pp. 01053
Author(s):  
Jian Chen ◽  
Danni Wang ◽  
Lin Qiao
Keyword(s):  
Data Storage ◽  
High Reliability ◽  
System Structure ◽  
Data Sources ◽  
Storage Device ◽  
Data Manipulation ◽  
Multiple Data ◽  
Actual Use ◽  
Data Source ◽  
Comparison And Analysis

This paper proposes the implementation of data storage structure with high reliability based on the characteristics of Oracle 12c. On the basis of micro application platform, the main advantages of data structure works out active-active or multi-active problems of hardware storage device and ensures that the business can still be able to use data source to carry on data manipulation under the circumstances of one or multiple data sources corruption, so as to guarantee the whole business without interruption. This paper makes detailed introduction of each module in the system structure, conducts brief description, comparison and analysis on disseminating algorithm of data manipulation and comparison algorithm of the same data source, and carries on detailed proof and explanation of the use of various algorithms in the actual use procedure.

scholarly journals MultiSourcDSim: an integrated approach for exploring disease similarity

2019 ◽  
Vol 19 (S6) ◽  
Author(s):  
Lei Deng ◽  
Danyi Ye ◽  
Junmin Zhao ◽  
Jingpu Zhang
Keyword(s):  
Integrated Approach ◽  
Data Sources ◽  
Related Data ◽  
The Past ◽  
Multiple Data ◽  
Disease Similarity ◽  
Disease Associations ◽  
Novel Method ◽  
Similarity Networks ◽  
Few Data

Abstract Background A collection of disease-associated data contributes to study the association between diseases. Discovering closely related diseases plays a crucial role in revealing their common pathogenic mechanisms. This might further imply treatment that can be appropriated from one disease to another. During the past decades, a number of approaches for calculating disease similarity have been developed. However, most of them are designed to take advantage of single or few data sources, which results in their low accuracy. Methods In this paper, we propose a novel method, called MultiSourcDSim, to calculate disease similarity by integrating multiple data sources, namely, gene-disease associations, GO biological process-disease associations and symptom-disease associations. Firstly, we establish three disease similarity networks according to the three disease-related data sources respectively. Secondly, the representation of each node is obtained by integrating the three small disease similarity networks. In the end, the learned representations are applied to calculate the similarity between diseases. Results Our approach shows the best performance compared to the other three popular methods. Besides, the similarity network built by MultiSourcDSim suggests that our method can also uncover the latent relationships between diseases. Conclusions MultiSourcDSim is an efficient approach to predict similarity between diseases.

Application of Dempster–Shafer Data Fusion Technique in Support of Decision Making with Big Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 32-37 ◽  
Author(s):  
Ping Yi ◽  
Songling Zhang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Fusion ◽  
Incomplete Information ◽  
Data Sources ◽  
Fusion Technique ◽  
Crowd Management ◽  
Multiple Data ◽  
Data Source ◽  
Single Data

This paper introduces applications of the Dempster–Shafer (D-S) data fusion technique in transportation system decision making. D-S inference is a statistics-based data classification technique, and it can be used when data sources contribute discontinuous and incomplete information and no single data source can produce an overwhelmingly high probability of certainty for identifying the most probable event. The technique captures and combines the information contributed by the data sources by using Dempster’s rule to find the conjunction of the events and to determine the highest associated probability. The D-S theory is explained and its implementation described through numerical examples of a ride-hauling service and of crowd management at a subway station. Results from the applications have shown that the technique is very effective in dealing with incomplete information and multiple data sources in the era of big data.

scholarly journals Arcitecture - inclusive culture: Protection of persons with disabilities

2015 ◽  
Vol 13 (1) ◽  
pp. 47-56
Author(s):  
Dragana Vasiljevic-Tomic ◽  
Radojko Obradovic
Keyword(s):  
Integrated Approach ◽  
Social Model ◽  
Individual Member ◽  
Persons With Disabilities ◽  
New Approach ◽  
Social Community ◽  
Fundamental Dimension ◽  
International Conventions

The paper has analyzed the contemporary standards that all democracies have embraced. A special emphasis has been placed on international conventions and rules, as well as the laws to protect the rights of all members of a modern social community. It is its duty to look after all its members and eliminate any form of discrimination, and even a threat of it. The European Concept for Accessibility promotes a new social model, designed to change entirely the attitude to the handicapped. The Concept offers a completely new approach, viewing the community as a whole. This integrated approach stretches to the needs of the community and the needs of each and every member of it, making no difference between individual groups within the population. Instead, it creates the environment to satisfy the needs of each individual member and, by extension, the community at large. The role of architecture is to recognize the needs of a community and take the lead in reshaping the standards to create a new environment accessible to all its members. This will give architecture a fundamental dimension, and make it the integrative factor to blend the community together.

Group Pattern Discovery Systems for Multiple Data Sources

2011 ◽  
pp. 546-549
Author(s):  
Shichao Zhang ◽  
Chengqi Zhang
Keyword(s):  
Pattern Analysis ◽  
Pattern Discovery ◽  
Data Sources ◽  
Multiple Data Source ◽  
Local Pattern ◽  
Multiple Data ◽  
Analysis Strategy ◽  
Data Source ◽  
Analysis System ◽  
Group Pattern

Multiple data source mining is the process of identifying potentially useful patterns from different data sources, or datasets (Zhang et al., 2003). Group pattern discovery systems for mining different data sources are based on local pattern-analysis strategy, mainly including logical systems for information enhancing, a pattern discovery system, and a post-pattern-analysis system.

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