A Temporal Clustering Algorithm for Achieving the Trade-off between the User Experience and the Equipment Economy in the Context of IoT

Smart Optimization of Proactive Control of Petroleum Reservoir

ENTRENOVA - ENTerprise REsearch InNOVAtion ◽

10.54820/asqf9458 ◽

2021 ◽

Vol 7 (1) ◽

pp. 304-313

Author(s):

Edyta Kuk ◽

Michał Kuk ◽

Damian Janiga ◽

Paweł Wojnarowski ◽

Jerzy Stopa

Keyword(s):

Artificial Intelligence ◽

Optimal Control ◽

Decision Tree ◽

Clustering Algorithm ◽

Petroleum Reservoirs ◽

Economic Benefits ◽

Petroleum Reservoir ◽

Cluster Assignment ◽

Economic Point ◽

Temporal Clustering

Artificial Intelligence plays an increasingly important role in many industrial applications as it has great potential for solving complex engineering problems. One of such applications is the optimization of petroleum reservoirs production. It is crucial to produce hydrocarbons efficiently as their geological resources are limited. From an economic point of view, optimization of hydrocarbon well control is an important factor as it affects the whole market. The solution proposed in this paper is based on state-of-the-art artificial intelligence methods, optimal control, and decision tree theory. The proposed idea is to apply a novel temporal clustering algorithm utilizing an autoencoder for temporal dimensionality reduction and a temporal clustering layer for cluster assignment, to cluster wells into groups depending on the production situation that occurs in the vicinity of the well, which allows reacting proactively. Then the optimal control of wells belonging to specific groups is determined using an auto-adaptive decision tree whose parameters are optimized using a novel sequential model-based algorithm configuration method. Optimization of petroleum reservoirs production translates directly into several economic benefits: reduction in operation costs, increase in the production effectiveness and increase in overall income without any extra expenditure as only control is changed. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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A Deep-Learning Parallel Processing Agglomerative Algorithm for the Identification of Distinct Seismic Regions in the Southern Hellenic Seismic Arc

10.5194/egusphere-egu2020-10768 ◽

2020 ◽

Author(s):

Alexandra Moshou ◽

Antonios Konstantaras ◽

Emmanouil Markoulakis ◽

Panagiotis Argyrakis ◽

Emmanouil Maravelakis

Keyword(s):

Deep Learning ◽

Parallel Processing ◽

Seismic Data ◽

Clustering Algorithm ◽

Expert Knowledge ◽

Geophysical Research ◽

International Conference ◽

Temporal Clustering ◽

Seismic Regions ◽

Spatio Temporal

The identification of distinct seismic regions and the extraction of features of theirs in relation to known underground fault mappings could provide most valuable information towards understanding the seismic clustering phenomenon, i.e. whether an earthquake occurring in a particular area can trigger another earthquake in the vicinity. This research paper works towards that direction and unveils the potential presence and extent of distinct seismic regions in the area of the Southern Hellenic Seismic Arc. To achieve that, a spatio-temporal clustering algorithm has been developed based on expert knowledge regarding the spatial and timely influence of an earthquake&#160; in its nearby vicinity using seismic data provided by the Geodynamics Institute of Athens, and is further supported by geological observations of underground faults&#8217; mappings beneath the addressed potentially distinct seismic regions. This is made possible thanks to advances in deep learning and graphics processing units&#8217; 3D technology that encompass parallel processing architectures, which comprise of blocks of multiple cores with parallel threads providing the necessary foundation in terms of hardware for accelerated processing for parallel seismic big data analysis. Seismic data are normally stored in massive continuously expanding matrices, as wide areas seismic covering is thickening, due to the establishment of denser recording networks, and decades of data are being stacked together. This research work embodies that technology for the development and implementation of a Cuda parallel processing agglomerative spatio-temporal clustering algorithm that enables the import of expert knowledge for the investigation of the potential presence of distinct seismic regions in the vicinity under investigation. The overall spatio temporal clustering results are also in accordance with empirical observations reported in the literature throughout the vicinity of the Hellenic Seismic Arc.Indexing terms: parallel processing, heterogeneous parallel programming, Cuda, distinct seismic regions, seismic clustering, spatio-temporal clusteringReferencesAxaridou A., I. Chrysakis, C. Georgis, M. Theodoridou, M. Doerr, A. Konstantaras, and E. Maravelakis. 3D-SYSTEK: Recording and exploiting the production workflow of 3D-models in cultural heritage. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 51-56, 2014.Drakatos G. and J. Latoussakis. A catalog of aftershock sequences in Greece (1971&#8211;1997): Their spatial and temporal characteristics. Journal of Seismology. 5, 137&#8211;145, 2001.Konstantaras A.J. Classification of distinct seismic regions and regional temporal modelling of seismicity in the vicinity of the Hellenic seismic arc. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6 (4), 1857-1863, 2012.Konstantaras A.J., E. Katsifarakis, E. Maravelakis, E. Skounakis, E. Kokkinos and E. Karapidakis. Intelligent spatial-clustering of seismicity in the vicinity of the Hellenic Seismic Arc. Earth Science Research 1 (2), 1-10, 2012.Maravelakis E., A. Konstantaras, K. Kabassi, I. Chrysakis, C. Georgis and A. Axaridou. 3DSYSTEK web-based point cloud viewer. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 262-266, 2014.Moshou Alexandra, Eleftheria Papadimitriou, George Drakatos, Christos Evangelidis Vasilios Karakostas, Filippos Vallianatos, and Konstantinos Makropoulos Focal Mechanisms at the convergent plate boundary in Southern Aegean, Greece, Geophysical Research Abstracts, Vol. 16, EGU2014-12185, 2014, EGU General Assembly 2014

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Nonnegative Matrix Factorization-Based Spatial-Temporal Clustering for Multiple Sensor Data Streams

Journal of Sensors ◽

10.1155/2014/824904 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12

Author(s):

Di-Hua Sun ◽

Chun-Yan Sang

Keyword(s):

Data Streams ◽

Clustering Algorithm ◽

Synthetic Data ◽

Spatial Relationship ◽

Nonnegative Matrix ◽

Research Area ◽

Sensor Data ◽

Temporal Clustering ◽

Multiple Sensor ◽

Active Research

Cyber physical systems have grown exponentially and have been attracting a lot of attention over the last few years. To retrieve and mine the useful information from massive amounts of sensor data streams with spatial, temporal, and other multidimensional information has become an active research area. Moreover, recent research has shown that clusters of streams change with a comprehensive spatial-temporal viewpoint in real applications. In this paper, we propose a spatial-temporal clustering algorithm (STClu) based on nonnegative matrix trifactorization by utilizing time-series observational data streams and geospatial relationship for clustering multiple sensor data streams. Instead of directly clustering multiple data streams periodically, STClu incorporates the spatial relationship between two sensors in proximity and integrates the historical information into consideration. Furthermore, we develop an iterative updating optimization algorithm STClu. The effectiveness and efficiency of the algorithm STClu are both demonstrated in experiments on real and synthetic data sets. The results show that the proposed STClu algorithm outperforms existing methods for clustering sensor data streams.

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A Dynamic Fuzzy Temporal Clustering for Imprecise Location Streams

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488517500179 ◽

2017 ◽

Vol 25 (03) ◽

pp. 409-426 ◽

Cited By ~ 2

Author(s):

J. Medina Quero ◽

M. D. Ruiz Lozano ◽

J. A. Castañeda García ◽

M. A. Rodriguez Molina ◽

D. M. Frias Jamilena

Keyword(s):

Information Processing ◽

Data Analysis ◽

Ubiquitous Computing ◽

Computer Science ◽

Mobile Application ◽

Ambient Intelligence ◽

Clustering Algorithm ◽

Urban Environments ◽

Fuzzy Evaluation ◽

Temporal Clustering

The clustering has provided data analysis in many contexts of Computer Science. It is widely applied in Ambient Intelligence and Ubiquitous Computing for information processing, with geolocation data prominently. In this paper, we introduce a dynamic fuzzy temporal clustering algorithm (DFTC) to detect stays of users in urban environments based on locations from imprecise sensors. Our approach includes fuzzy evaluation of temporal and probabilistic data providing analysis in real time. As results, we have developed a mobile application which integrates the DFTC and detects satisfactorily user stays related to urban commerces from a real environment.

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Fuzzy Entropy-Based Spatial Hotspot Reliability

Entropy ◽

10.3390/e23050531 ◽

2021 ◽

Vol 23 (5) ◽

pp. 531

Author(s):

Ferdinando Di Martino ◽

Salvatore Sessa

Keyword(s):

Clustering Algorithm ◽

Geographical Area ◽

Fuzzy Entropy ◽

Circular Area ◽

Analysis Problem ◽

Trade Off ◽

Fuzzy C Means ◽

Good Trade ◽

Disease Analysis ◽

Fuzzy C Means Algorithm

Cluster techniques are used in hotspot spatial analysis to detect hotspots as areas on the map; an extension of the Fuzzy C-means that the clustering algorithm has been applied to locate hotspots on the map as circular areas; it represents a good trade-off between the accuracy in the detection of the hotspot shape and the computational complexity. However, this method does not measure the reliability of the detected hotspots and therefore does not allow us to evaluate how reliable the identification of a hotspot of a circular area corresponding to the detected cluster is; a measure of the reliability of hotspots is crucial for the decision maker to assess the need for action on the area circumscribed by the hotspots. We propose a method based on the use of De Luca and Termini’s Fuzzy Entropy that uses this extension of the Fuzzy C-means algorithm and measures the reliability of detected hotspots. We test our method in a disease analysis problem in which hotspots corresponding to areas where most oto-laryngo-pharyngeal patients reside, within a geographical area constituted by the province of Naples, Italy, are detected as circular areas. The results show a dependency between the reliability and fluctuation of the values of the degrees of belonging to the hotspots.

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TEXT SUMMARIZER USING CLUSTERING TECHNIQUES AND ANOMALIES DETECTION ON SVM

EPRA International Journal of Research & Development (IJRD) ◽

10.36713/10.36713/epra3758 ◽

2019 ◽

pp. 1-7

Author(s):

Oyinloye Oghenerukevwe Elohor ◽

Adesoji susan ◽

Akinbohun Folake

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Clustering Algorithm ◽

Text Summarization ◽

Support Vector ◽

Trade Off ◽

Clustering Techniques ◽

Svm Classification ◽

Knowledge Based ◽

Current Affairs

The study is aimed at developing a text summarizer using clustering and anomalies detection with SVM classification. A text summarization approach is proposed which uses the SVM clustering algorithm. The proposed project can be used to summarize articles from fields as diverse as politics, sports, current affairs, finance and any other explanatory document. However, it does cause a trade-off between domain independence and a knowledge-based summary which would provide data in a form more easily understandable to the user. A bundle of libraries and software’s was utilized for proper text summary of alphanumeric entering. KEYWORDS— Anomalies detection, SVM (support vector machine), clustering, text summarization, data mining

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Awareness Modeling and Computing for Quality-Aware Coordination

Mathematical Problems in Engineering ◽

10.1155/2021/6782664 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Qing Liu ◽

Charles Z. Liu ◽

Lan-lan Li ◽

Maria T. Gambino

Keyword(s):

User Experience ◽

Network Performance ◽

Learning Strategy ◽

Sufficient Conditions ◽

Data Communication ◽

Subspace Learning ◽

Assessment Model ◽

Test Quality ◽

Trade Off ◽

Close Form

In this paper, we address the issues of the trade-off between QoS and QoE with an analytical analysis based on mathematical modeling under a unified normalization measurement. We model through computation the awareness of QoS and QoE with a strategy of quality-aware QoE-QoS coordination. A balanced coordination is proposed using modeling correlations between user experience and service performance. The main contributions of this paper include three main parts. First, a comprehensive mapping is modeled in a close form to illustrate the analytic correlations between QoS, QoE, and data communication. Second, an analytical method to analyze and coordinate the nonlinear trade-off between QoE and QoS is proposed based on the theoretical proof with discussions on necessary-sufficient conditions. Third, an algorithmic framework is provided to perform QoE-QoS coordination based on quality-awareness computing with a test proof. An assessment model for user experience quantification is built with the mean opinion score (MOS) test. Quality-aware QoE and QoS models are built based on the subspace learning strategy. Simulations are given to prove the feasibility and effectiveness of the proposed method. The results show that the operations with the proposed solution can be obtained analytically with balanced efficiency in both user experience performance and network performance.

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User Driven Query Framework of Social Networks for Geo-Temporal Analysis of Events of Interest

Advances in Data Mining and Database Management - Handbook of Research on Innovative Database Query Processing Techniques ◽

10.4018/978-1-4666-8767-7.ch008 ◽

2015 ◽

pp. 224-249 ◽

Cited By ~ 1

Author(s):

Gloria Bordogna ◽

Simone Sterlacchini ◽

Paolo Arcaini

Keyword(s):

Social Networks ◽

Social Media ◽

Clustering Algorithm ◽

Temporal Distribution ◽

Temporal Analysis ◽

Local Scale ◽

Temporal Clustering ◽

Temporal Query ◽

Spatio Temporal ◽

Alternative Routes

In this chapter we propose a framework for collecting, organizing into a database and querying information in social networks by the specification of content-based, geographic and temporal conditions to the aim of detecting periodic and aperiodic events. Our proposal could be a basis for developing context aware services. For example to identify the streets and their rush hours by analyzing the messages in social media periodically sent by queuing drivers and to report these critical spatio-temporal situations to help other drivers to plan alternative routes. Specifically, we rely on a focused crawler to periodically collect messages in social networks related with the contents of interest, and on an original geo-temporal clustering algorithm in order to explore the geo-temporal distribution of the messages. The clustering algorithm can be customized so as to identify aperiodic and periodic events at global or local scale based on the specification of geographic and temporal query conditions.

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Research on Data Packets Clustering Algorithm in the Wireless Multiple Hop Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.651-653.1905 ◽

2014 ◽

Vol 651-653 ◽

pp. 1905-1908 ◽

Cited By ~ 1

Author(s):

He Wei Zhang ◽

Lei Sun ◽

Hong Zhang

Keyword(s):

Energy Consumption ◽

Service Quality ◽

Data Transmission ◽

Clustering Algorithm ◽

Network Node ◽

Clustering Method ◽

Trade Off ◽

Data Packets ◽

Data Efficiency ◽

Node Energy

In view of the problems existing in the wireless multiple hop network such as consumption imbalance of node power, disunity of node transmission data efficiency, unfixed life within the scope of network, it has put forward the trade-off relationship between the wireless multiple hop network node energy consumption and multiple factors based on the k-means clustering method. The principle of steps and characteristics of the k-means clustering algorithm are first introduced; Then model the influence of the K-means polymerization on VoIP service quality, then use the k-means clustering method to make cluster analysis for network node data package, and mine the trade-off relationship between data transmission service quality and multiple hops node energy consumption; Finally carry on the simulation experiment to test the performance of this method. Simulation results show that the method not only improves the data transmission service quality of VoIP service, but also reduces the energy consumption of nodes and prolongs the life span of the wireless network.

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