A New Fuzzy Bayesian Clustering Algorithm to Enhance Radio Planning Tool Predictions

Summary We present and test a new screening methodology to discriminate among alternative and competing enhanced-oil-recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques was successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests before fieldwide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Because similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria, we consider fluid (density and viscosity) and reservoir-formation (porosity, permeability, depth, and temperature) properties. Our approach is observation-driven and grounded on an exhaustive database that we compiled after considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through principal-component analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesian-clustering algorithm. Considering the cluster that includes the EOR field under evaluation, an intercluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components (PCs) and (b) the fraction of variance associated with each PC is taken as weight of the Euclidean distance that we determine. As a test bed, we apply our approach on three fields operated by Eni. These include light-, medium-, and heavy-oil reservoirs, where gas, chemical, and thermal EOR projects were, respectively, proposed. Our results are (a) conducive to the compilation of a broad and extensively usable database of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.

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RADIO ACCESS NETWORK REQUIREMENT FOR NEW DEPLOYMENT OF WIMAX IN DHAKA

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2016.1362 ◽

2016 ◽

pp. 175-179

Author(s):

Tawhid Kawser ◽

MOHAMMED R. AL-AMIN ◽

KHONDOKER Z. ISLAM ◽

SIFAT-E- MOHAMMAD

Keyword(s):

High Speed ◽

Access Network ◽

Mobile Wimax ◽

Radio Network ◽

Planning Tool ◽

Radio Access ◽

High Speed Data ◽

Simulation Results ◽

Radio Planning

Mobile WiMAX is expected to be the next generation radio-interface, complementing WLAN and challenging EVDO/HSPA/LTE. High speed data rate, reduced latency, better Quality of service, and mobility can allow WiMAX to meet the rapidly growing demand of the users. A study of WiMAX Radio Network Planning (RNP) for an urban area like Dhaka city in Bangladesh is presented in this paper in order to help predetermine the radio access infrastructure requirements. A suitable radio planning tool has been used for this purpose. The simulation results of throughput and Carrier to Interference plus Noise Ratio (CINR) are provided.

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A simulation-based evaluation of STRUCTURE software for exploring the introduction routes of invasive species

10.1101/094029 ◽

2016 ◽

Cited By ~ 2

Author(s):

Eric Lombaert ◽

Thomas Guillemaud ◽

Emeline Deleury

Keyword(s):

Invasive Species ◽

Population Genetic ◽

Clustering Algorithm ◽

Bayesian Clustering ◽

Demographic Parameters ◽

Native Population ◽

Introduced Populations ◽

Large Numbers ◽

Simulation Based ◽

Second Category

AbstractPopulation genetic methods are widely used to retrace the introduction routes of invasive species. The unsupervised Bayesian clustering algorithm implemented in STRUCTURE is amongst the most frequently use of these methods, but its ability to provide reliable information about introduction routes has never been assessed. We used computer simulations of microsatellite datasets to evaluate the extent to which the clustering results provided by STRUCTURE were misleading for the inference of introduction routes. We focused on the simple case of an invasion scenario involving one native population and two independently introduced populations, because it is the sole scenario with two introduced populations that can be rejected when obtaining a particular clustering with a STRUCTURE analysis at K = 2 (two clusters). Results were classified as “misleading” or “non-misleading”. We then investigated the influence of two demographic parameters (effective size and bottleneck severity) and different numbers of loci on the type and frequency of misleading results. We showed that misleading STRUCTURE results were obtained for 10% of our simulated datasets and at a frequency of up to 37% for some combinations of parameters. Our results highlighted two different categories of misleading output. The first occurs in situations in which the native population has a low level of diversity. In this case, the two introduced populations may be very similar, despite their independent introduction histories. The second category results from convergence issues in STRUCTURE for K = 2, with strong bottleneck severity and/or large numbers of loci resulting in high levels of differentiation between the three populations.

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Design, Implementation, and Empirical Validation of an IoT Smart Irrigation System for Fog Computing Applications Based on LoRa and LoRaWAN Sensor Nodes

Sensors ◽

10.3390/s20236865 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6865

Author(s):

Iván Froiz-Míguez ◽

Peio Lopez-Iturri ◽

Paula Fraga-Lamas ◽

Mikel Celaya-Echarri ◽

Óscar Blanco-Novoa ◽

...

Keyword(s):

Energy Consumption ◽

Irrigation System ◽

Power Level ◽

Fog Computing ◽

Sensor Nodes ◽

Sensitivity Threshold ◽

Area Network ◽

Planning Tool ◽

The Impact ◽

Radio Planning

Climate change is driving new solutions to manage water more efficiently. Such solutions involve the development of smart irrigation systems where Internet of Things (IoT) nodes are deployed throughout large areas. In addition, in the mentioned areas, wireless communications can be difficult due to the presence of obstacles and metallic objects that block electromagnetic wave propagation totally or partially. This article details the development of a smart irrigation system able to cover large urban areas thanks to the use of Low-Power Wide-Area Network (LPWAN) sensor nodes based on LoRa and LoRaWAN. IoT nodes collect soil temperature/moisture and air temperature data, and control water supply autonomously, either by making use of fog computing gateways or by relying on remote commands sent from a cloud. Since the selection of IoT node and gateway locations is essential to have good connectivity and to reduce energy consumption, this article uses an in-house 3D-ray launching radio-planning tool to determine the best locations in real scenarios. Specifically, this paper provides details on the modeling of a university campus, which includes elements like buildings, roads, green areas, or vehicles. In such a scenario, simulations and empirical measurements were performed for two different testbeds: a LoRaWAN testbed that operates at 868 MHz and a testbed based on LoRa with 433 MHz transceivers. All the measurements agree with the simulation results, showing the impact of shadowing effects and material features (e.g., permittivity, conductivity) in the electromagnetic propagation of near-ground and underground LoRaWAN communications. Higher RF power levels are observed for 433 MHz due to the higher transmitted power level and the lower radio propagation losses, and even in the worst gateway location, the received power level is higher than the sensitivity threshold (−148 dBm). Regarding water consumption, the provided estimations indicate that the proposed smart irrigation system is able to reduce roughly 23% of the amount of used water just by considering weather forecasts. The obtained results provide useful guidelines for future smart irrigation developers and show the radio planning tool accuracy, which allows for optimizing the sensor network topology and the overall performance of the network in terms of coverage, cost, and energy consumption.

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Gaussian Hierarchical Bayesian Clustering Algorithm

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007) ◽

10.1109/isda.2007.4389598 ◽

2007 ◽

Author(s):

Rafael Eduardo Ruviaro Christ ◽

Edwin Villanueva Talavera ◽

Carlos Dias Maciel

Keyword(s):

Clustering Algorithm ◽

Bayesian Clustering ◽

Hierarchical Bayesian

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Characterization of a Bayesian genetic clustering algorithm based on a Dirichlet process prior and comparison among Bayesian clustering methods

BMC Bioinformatics ◽

10.1186/1471-2105-12-263 ◽

2011 ◽

Vol 12 (1) ◽

Cited By ~ 13

Author(s):

Akio Onogi ◽

Masanobu Nurimoto ◽

Mitsuo Morita

Keyword(s):

Dirichlet Process ◽

Clustering Algorithm ◽

Bayesian Clustering ◽

Clustering Methods ◽

Dirichlet Process Prior ◽

Genetic Clustering

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Design and Empirical Validation of a LoRaWAN IoT Smart Irrigation System

Proceedings ◽

10.3390/ecsa-6-06540 ◽

2020 ◽

Vol 42 (1) ◽

pp. 62 ◽

Cited By ~ 2

Author(s):

Paula Fraga-Lamas ◽

Mikel Celaya-Echarri ◽

Leyre Azpilicueta ◽

Peio Lopez-Iturri ◽

Francisco Falcone ◽

...

Keyword(s):

Energy Consumption ◽

Urban Areas ◽

Irrigation System ◽

Irrigation Schedule ◽

Operating Conditions ◽

Sensor Data ◽

Direct Access ◽

Planning Tool ◽

Long Distance ◽

Radio Planning

In some parts of the world, climate change has led to periods of drought that require managing efficiently the scarce water and energy resources. This paper proposes an IoT smart irrigation system specifically designed for urban areas where remote IoT devices have no direct access to the Internet or to the electrical grid, and where wireless communications are difficult due to the existence of long distances and multiple obstacles. To tackle such issues, this paper proposes a LoRaWAN-based architecture that provides long distance and communications with reduced power consumption. Specifically, the proposed system consists of IoT nodes that collect sensor data and send them to local fog computing nodes or to a remote cloud, which determine an irrigation schedule that considers factors such as the weather forecast or the moist detected by nearby nodes. It is essential to deploy the IoT nodes in locations within the provided coverage range and that guarantee good speed rates and reduced energy consumption. Due to this reason, this paper describes the use of an in-house 3D-ray launching radio-planning tool to determine the best locations for IoT nodes on a real medium-scale scenario (a university campus) that was modeled with precision, including obstacles such as buildings, vegetation, or vehicles. The obtained simulation results were compared with empirical measurements to assess the operating conditions and the radio planning tool accuracy. Thus, it is possible to optimize the wireless network topology and the overall performance of the network in terms of coverage, cost, and energy consumption.

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