scholarly journals Exploiting the RSSI Long-Term Data of a WSN for the RF Channel Modeling in EPS Environments

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3076
Author(s):  
Roddy A. R. Antayhua ◽  
Maicon D. Pereira ◽  
Nestor C. Fernandes ◽  
Fernando Rangel de Sousa
Keyword(s):  
Performance Optimization ◽  
Electromagnetic Interference ◽  
Large Scale ◽  
Power Level ◽  
Hydroelectric Power Plant ◽  
Channel Modeling ◽  
Electric Power System ◽  
Hydroelectric Power ◽  
Term Data

In this paper, we propose a methodology to use the received signal strength indicator (RSSI) available by the protocol stack of an installed Wireless Sensor Network (WSN) at an electric-power-system environment (EPS) as a tool for obtaining the characteristic of its communication channel. Thereby, it is possible to optimize the settings and configuration of the network after its deployment, which is usually run empirically without any previous knowledge of the channel. A study case of a hydroelectric power plant is presented, where measurements recorded over a two-month period were analyzed and treated to obtain the large-scale characteristics of the radiofrequency channel at 2.4 GHz. In addition, we showed that instantaneous RSSI data can also be used to detect specific issues in the network, such as repetitive patterns in the transmitted power level of the nodes, and information about its environment, such as the presence of external sources of electromagnetic interference. As a result, we demonstrate the practical use of the RSSI long-term data generated by the WSN for its own performance optimization and the detection of particular events in an EPS or any similar industrial environment.

Assessment of the technical condition of underground structures of the largest hydroelectric power station in the North Caucasus, the Chirkeyskaya HPP

2021 ◽  
Vol 18 (3) ◽  
pp. 410-420
Author(s):  
Vladimir N. KAVKAZKY ◽  
◽  
Yana V. MEL’NIK ◽  
Alexey P. LEIKIN ◽  
Andrey V. BENIN ◽  
...  
Keyword(s):  
Large Scale ◽  
Practical Importance ◽  
Hydroelectric Power Plant ◽  
Technical Condition ◽  
Hydroelectric Power Station ◽  
Hydroelectric Power ◽  
Underground Structures ◽  
North Caucasus ◽  
The Caucasus ◽  
The North

Objective: Chirkeyskaya HPP is by far the most powerful hydroelectric power plant in the North Caucasus with the highest arched dam in Russia and the second highest dam in the country after the Sayano-Shushenskaya HPP. This explains why it is called the pearl of the Caucasus. Methods: For the operation and maintenance of this unique structure, a large-scale complex of underground structures for various purposes was built, the technical condition of which must be constantly monitored. To carry out work on the survey of underground structures, the management of the design and survey institute of JSC “Lengidroproekt” decided to attract specialists from the Department of Tunnels and Subways and the Test Center “Strength” of Emperor Alexander I Petersburg State Transport University. The work was successfully carried out at the end of 2015. Results: The safety of underground structures was objectively assessed. Recommendations for the repair and further comprehensive reconstruction of the Chirkeyskaya HPP have been developed. Practical importance: Carry out work on the survey of underground structures of Chirkeyskaya HPP is allowes elaborate of complex measures on safety from Chirkeyskaya HPP.

scholarly journals Variability of seasonal and annual rainfall in the River Nile riparian countries and possible linkages to ocean–atmosphere interactions

2015 ◽  
Vol 47 (1) ◽  
pp. 171-184 ◽  
Author(s):  
Charles Onyutha
Keyword(s):  
Large Scale ◽  
Equatorial Region ◽  
Annual Rainfall ◽  
River Nile ◽  
Short Term ◽  
Ocean Atmosphere ◽  
Data Points ◽  
The Difference ◽  
Term Data

Variability analyses for the rainfall over the Nile Basin have been confined mostly to sub-basins and the annual mean of the hydroclimatic variable based on observed short-term data from a few meteorological stations. In this paper, long-term country-wide rainfall over the period 1901–2011 was used to assess variability in the seasonal and annual rainfall volumes in all the River Nile countries in Africa. Temporal variability was determined through temporal aggregation of series rescaled nonparametrically in terms of the difference between the exceedance and non-exceedance counts of data points such that the long-term average (taken as the reference) was zero. The co-occurrence of the variability of rainfall with those of the large-scale ocean–atmosphere interactions was analyzed. Between 2000 and 2012, while the rainfall in the equatorial region was increasing, that for the countries in the northern part of the River Nile was below the reference. Generally, the variability in the rainfall of the countries in the equatorial (northern) part of the River Nile was found to be significantly linked to occurrences in the Indian and Atlantic (Pacific and Atlantic) Oceans. Significant linkages to Niño 4 regarding the variability of both the seasonal and annual rainfall of some countries were also evident.

Critical Review and Appraisal of Traditional and New Procedures for the Quantification of Creep Fracture Behavior Using 1Cr–1Mo–0.25V Steel

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
M. Evans
Keyword(s):  
Large Scale ◽  
Rupture Life ◽  
Design Data ◽  
Data Set ◽  
New Approach ◽  
Creep Fracture ◽  
Scale Test ◽  
New Procedures ◽  
Term Data

The approaches traditionally used to quantify creep and creep fracture are critically assessed and reviewed in relation to a new approach proposed by Wilshire and Scharning. The characteristics, limitations, and predictive accuracies of these models are illustrated by reference to information openly available for the bainitic 1Cr–1Mo–0.25V steel. When applied to this comprehensive long-term data set, the estimated 100,000–300,000 h strength obtained from the older so called traditional methods varied considerably. Further, the isothermal predictions from these models became very unstable beyond 100,000 h. In contrast, normalizing the applied stress through an appropriate ultimate tensile strength value not only reduced the melt to melt scatter in rupture life, but also the 100,000 h strengths determined from this model for this large scale test program are predicted very accurately by extrapolation of creep life measurements lasting less than 5000 h. The approach therefore offers the potential for reducing the scale and cost of current procedures for acquisition of long-term engineering design data.

Power Plant Performance Based on Power-Efficiency Curve of Hydroelectric Power Plant

2015 ◽  
Vol 785 ◽  
pp. 516-520 ◽  
Author(s):  
Anis Shazwani Zulkifli ◽  
Noor Miza Muhamad Razali ◽  
Marayati Marsadek ◽  
Zainuddin Yahya ◽  
Tengku Juhana Tengku Hashim
Keyword(s):  
Power Plant ◽  
Power Efficiency ◽  
Large Scale ◽  
Good Condition ◽  
Hydroelectric Power Plant ◽  
Plant Performance ◽  
Hydropower Plant ◽  
Hydroelectric Power ◽  
Performance Curve ◽  
Efficiency Curve

—Hydropower energy is widely used throughout the world. It is the only renewable energy that is presently commercially practical on the large scale. In order to maintain the hydropower plant in good condition, the performance of the power plant needs to be monitored constantly. Efficiency curve helps in studying the performance of the turbine under various conditions and this is the best way to look for the performance of the power plant. Therefore, this paper presents the relationship between load (MW) and efficiency of each turbine and generator unit. This project uses Microsoft Excel 2010 software to produce a graph from the exact data produced from the database. This paper’s objective is to compare the theoretical performance curve and the calculated performance curve and also to discuss the hydroelectric power plant performance.

Initiating & Managing Long-Term Data with Amateur Scientists

2017 ◽  
Vol 79 (1) ◽  
pp. 28-34
Author(s):  
Will H. Ryan ◽  
Elise S. Gornish ◽  
Lynn Christenson ◽  
Stacey Halpern ◽  
Sandra Henderson ◽  
...  
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Data Availability ◽  
Outreach Program ◽  
Interest In Science ◽  
Large Scale Data ◽  
Funding Agencies ◽  
Scale Data ◽  
Term Data

The value of long-term data (generally >10 years) in ecology is well known. Funding agencies clearly see the value in these data and have supported a limited number of projects to this end. However, individual researchers often see the challenges of long-term data collection as insurmountable. We propose that long-term data collection can be practical as part of any teaching or outreach program, and we provide guidance on how long-term projects can fit into a teaching and research schedule. While our primary audience is college faculty, our message is appropriate for anyone interested in establishing long-term studies. The benefits of adopting these kinds of projects include experience for students, encouraging public interest in science, increased publication potential for researchers, and increased large-scale data availability, leading to a better understanding of ecological phenomena.

scholarly journals Predict and Measure Air Quality Monitoring System Using Machine Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 2562-2571
Author(s):  
D.Saravanan , Et. al.
Keyword(s):  
Air Quality ◽  
Large Scale ◽  
Quality Monitoring ◽  
Suitable Model ◽  
Air Quality Monitoring ◽  
Related Data ◽  
Multiple Tasks ◽  
Standard Regularization ◽  
Term Data

This article looks at how artificial intelligence can help expect the hourly consolidation of air toxinSulphur ozone, element matter (PM2.5), and Sulphur dioxide. As one of the most excellently procedures, AI can efficiently prepare a model on a large amount of data by using large-scale streamlining computations. Even thoughseveral works use AI to predict air quality, most of the earlier studies are limited to long-term data and easilyinstruct regular relapse designs (direct or nonlinear) to expect the hourly air pollution focus. This paper suggestsadvanced analysis to simulate the hourly environmental change focus based on previous days' weather-related data by calculating the expectation for more than 24 hours as an execute multiple tasks learning (MTL) issue. This allows us to choose a suitable model with a variety of regularization strategies. We suggest a useful regularization that maintains the assumption patterns of concurrent hours to be nearby to each other, and we evaluate it to a few common MTL expect completion such as normal Frobenius standard regularization, normal atomicregularization, and '2,1-standard regularization. Our tests revealed that the suggested boundary declining concepts and constant hour-related regularizations outperform open product relapse models and regularizations in terms of execution.

scholarly journals Evidenciação Ambiental: uma análise dos relatórios de sustentabilidade da Usina Hidrelétrica Itaipu com base na Global Reporting Initiative – GRI ## Environmental Disclosure: an analysis of the sustainability reports of the Itaipu Hydroelectric Plant based on the Global Reporting Initiative – GRI

2021 ◽  
Vol 10 (2) ◽  
pp. 288
Author(s):  
Regiane Valejo Maciel ◽  
Carlos Jaelso Albanese Chaves ◽  
Giuliano Oliveira De Macedo
Keyword(s):  
Power Plant ◽  
Large Scale ◽  
Hydroelectric Plant ◽  
Hydroelectric Power Plant ◽  
Global Reporting Initiative ◽  
Hydroelectric Power ◽  
Social Relevance ◽  
Environmental Disclosure ◽  
Sustainability Reports

Considerando que o setor de hidrelétricas causa impactos ambientais de larga escala e que a evidenciação ambiental vem se tornando cada vez mais relevante para as organizações, a presente pesquisa teve por objetivo avaliar a qualidade da evidenciação ambiental praticada nos relatórios de sustentabilidade da Usina Hidrelétrica Binacional Itaipu. Quanto à abordagem, este estudo fundamenta-se na pesquisa qualitativa, e quanto ao objeto, este estudo se classifica como uma pesquisa documental. Para atingir o objetivo desta pesquisa, a coleta dos dados foi proporcionada pelos relatórios de sustentabilidade da Usina Hidrelétrica Itaipu do período de 2014 a 2019, a partir da confrontação dos princípios apresentados pela Global Reporting Initiative para a elaboração do relatório com o conteúdo dos relatórios divulgados pela Itaipu. A análise dos relatórios indica que a Itaipu apresentou falhas quanto a aderência do princípio “comparabilidade”, ao omitir informação sobre os valores investidos nos projetos analisados, fazendo com que a leitura do stakeholder ficasse prejudicada, entretanto, é possível identificar que a Itaipu buscou manter-se alinhada com os demais princípios analisados. O presente estudo contribuiu por buscar preencher a lacuna sobre a análise de evidenciação ambiental de uma organização de relevância social como a Itaipu Binacional.ABSTRACTConsidering that the hydroelectric sector causes large-scale environmental impacts, and that environmental disclosure is becoming increasingly relevant for organizations, this research aimed to evaluate the quality of environmental disclosure practiced in the sustainability reports of the Binational Itaipu Hydroelectric Power Plant . As for the approach, this study is based on qualitative research, and as for the object, this study is classified as documentary research. To achieve the objective of this research, data collection was provided by the sustainability reports of the Itaipu Hydroelectric Power Plant from 2014 to 2019, based on the confrontation of the principles presented by the Global Reporting Initiative guidelines with the content of the reports disclosed by Itaipu. The analysis of the reports indicates that Itaipu failed to adhere to the "comparability" principle, by omitting information on the amounts invested in the projects analyzed, causing the stakeholder reading to be impaired, however, it is possible to identify that Itaipu sought to maintain aligned with the other principles analyzed. This study contributed by seeking to fill the gap on the analysis of environmental disclosure in an organization of social relevance such as Itaipu Binational.

scholarly journals Visualization and normalization of drift effect across batches in metabolome-wide association studies

2020 ◽  
Author(s):  
Nasim Bararpour ◽  
Federica Gilardi ◽  
Cristian Carmeli ◽  
Jonathan Sidibe ◽  
Julijana Ivanisevic ◽  
...  
Keyword(s):  
Large Scale ◽  
Biomarker Discovery ◽  
Association Studies ◽  
Model Performance ◽  
Analytical Signal ◽  
Biologically Relevant ◽  
Signal Drift ◽  
Best Fitting ◽  
Term Data

AbstractAs a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and identification of metabolites having regulatory effect in various biological processes. While MS-based metabolomics assays are endowed with high-throughput and sensitivity, large-scale MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of true biologically relevant changes.We developed “dbnorm”, a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect dataset structure, at both macroscopic (sample batch) and microscopic (metabolic features) scales. To compare model performance on data correction, “dbnorm” assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how “dbnorm” efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.

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