scholarly journals Comparison of parameters of q-switching saturable absorbers estimated by different models and the impact of accuracy of input data on the results of the estimation

2014 ◽  
Vol 36 (5) ◽  
pp. 867-872 ◽  
Author(s):  
J. Młyńczak ◽  
K. Kopczyński
Keyword(s):  
Input Data ◽  
Saturable Absorbers ◽  
Q Switching ◽  
The Impact

scholarly journals Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1648
Author(s):  
Marinko Barukčić ◽  
Toni Varga ◽  
Vedrana Jerković Jerković Štil ◽  
Tin Benšić
Keyword(s):  
Power System ◽  
Power Management ◽  
Computational Intelligence ◽  
Input Data ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Distributed Generations ◽  
Data Resolution ◽  
The Impact

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

The Impact of Macroeconomic Factors on Environmental Loss Ratio: Evidence from Taiwanese Insurance Data

2018 ◽  
Vol 21 (03) ◽  
pp. 1850020
Author(s):  
Li-Hua Lai ◽  
Ching-Hao Chen ◽  
Tung-Cheng Chang
Keyword(s):  
Input Data ◽  
Economic Loss ◽  
Environmental Condition ◽  
Loss Ratio ◽  
Negative Effects ◽  
Macroeconomic Factors ◽  
Macroeconomic Factor ◽  
Environmental Loss ◽  
The Impact ◽  
Insurance Data

Environmental insurance (EI) protections help resolve the firm-industry economic loss problem. However, the loss ratio of EI is positively affected by itself from one period ahead. The positive and negative effects of macroeconomic factor on the loss ratio of EIs are not necessarily consistent, but they are dependent on the effect of the year’s environmental condition. The economic variables affecting the loss ratio of EI are quite inconsistent, so insurance prices and liability reserves should be modified every year. While the investigations are the special properties of our input data of Taiwan, the prescription of this paper could provide cross-references with other countries.

scholarly journals Application of the SWAT (Soil and Water Assessment Tool) model in the Ronnea catchment of Sweden

2013 ◽  
Vol 7 (3) ◽  
pp. 252-257
Keyword(s):  
Input Data ◽  
Management Practices ◽  
Assessment Tool ◽  
Swat Model ◽  
Soil Parameters ◽  
Daily Step ◽  
Calibration And Validation ◽  
The Impact ◽  
Water Assessment ◽  
Soil And Water

The subject of this article is the estimation of quantitative (hydrological) and qualitative parameters in the catchment of Ronnea (1800 Km2, located in south western Sweden) through the application of the Soil and Water Assessment Tool (SWAT). SWAT is a river basin model that was developed for the U.S.D.A. Agricultural Research Service, by the Blackland Research Center in Texas. The SWAT model is a widely known tool that has been used in several cases world-wide. It has the ability to predict the impact of land management practices on water, sediment and agricultural chemical yield in large complex watersheds. The present work investigates certain capabilities of the SWAT model which have not identified up to now. More in specific, the main targets of the work carried out are the following: • Identification of the existing hydrological and qualitative conditions • Preparation - Processing of data required to be used as input data of the model • Hydrological calibration - validation of the model, in 7 subbasins of the Catchment of Ronnea • Estimation and evaluation of the simulated qualitative parameters of the model All available data were offered by the relevant Institutes of Sweden, in the framework of the European program EUROHARP. The existing conditions in the catchment of Ronnea, are described in detail including topography, land uses, soil types, pollution sources, agricultural management practices, precipitation, temperature, wind speed, humidity, solar radiation as well as observed discharges and Nitrogen and Phosphorus substances concentrations. Most of the above data were used as input data for the application of SWAT model. Adequate methods were also used to complete missing values in time series and estimate additional parameters (such as soil parameters) required by the model. Hydrological calibration and validation took place for each outlet of the 7 subbasins of Ronnea catchment in an annual, monthly and daily step. The calibration was achieved by estimating parameters related to ground water movement and evaluating convergence between simulated and observed discharges by using mainly the Nash & Sutcliffe coefficient (NTD). Through the sensitivity analysis, main parameters of the hydrological simulation, were detected. According to the outputs of the SWAT model, the water balance of Ronnea catchment was also estimated. Hydrological calibration and validation is generally considered sufficient in an annual and monthly step. Hydrological calibration – validation in daily step, generally does not lead to high values of the NTD indicator. However, when compared to results obtained by the use of SWAT in Greece, a relatively high value of NTD is achieved in one subbasin. Finally, a comparison between the simulated and observed concentrations of total Phosphorus and Nitrogen was carried out.

scholarly journals State policy on the functioning of institute of bankruptcy in Ukraine

2019 ◽  
Vol 2 (4) ◽  
pp. 86-104
Author(s):  
Yuliya Orlovska ◽  
Nika Ilkova
Keyword(s):  
State Policy ◽  
Economic Freedom ◽  
Input Data ◽  
State Regulation ◽  
The Other ◽  
Main Task ◽  
Doing Business ◽  
National Economic ◽  
The Impact ◽  
Positive Effect

It is precisely in the course of adjusting the activities of these subjects, the main task of state regulation of the bankruptcy institute is the formation of such conditions for the functioning of the national economy, which will reduce the risk of doing business for all its entities and promote the internal reorganization of its structure in accordance with the requirements of global transformations. The system of indicators describing the situation in a certain area of ​​the functioning of national economic entities allows us to determine, directly or indirectly, the effectiveness of the bankruptcy institute at the macro-level. To analyze the impact of each of the factors on GDP, a sensitivity analysis was conducted according to which input data X were recorded at the values ​​of 2018 and alternately changed by 10%. For each such change, GDP was calculated as compared to the model value for 2018. As a result of the calculations, the most sensitive factors were identified and features of the functioning of the bankruptcy institute in the Ukrainian economy were identified. The main provisions of a state policy aimed at increasing the functional effectiveness of the bankruptcy institute are formulated. First of all, it is necessary to promote the country's position in the Doing business rankings, as well as the Indexes of Economic Freedom and Corruption Perceptions. On the other hand, an annual growth of the inflation index of around 10% and the level of the fiscal tax burden will also have a positive effect on GDP dynamics.

scholarly journals Uncertainties in Coastal Flood Risk Assessments in Small Island Developing States

2019 ◽  
Author(s):  
Matteo U. Parodi ◽  
Alessio Giardino ◽  
Ap van Dongeren ◽  
Stuart G. Pearson ◽  
Jeremy D. Bricker ◽  
...  
Keyword(s):  
Flood Risk ◽  
Input Data ◽  
Flood Damage ◽  
Small Island ◽  
Small Island Developing States ◽  
Damage Functions ◽  
Current Time ◽  
Coastal Flood ◽  
Coastal Flood Risk ◽  
The Impact

Abstract. Considering the likely increase of coastal flooding in Small Island Developing States (SIDS), coastal managers at the local and global level have been developing initiatives aimed at implementing Disaster Risk Reduction (DRR) measures and adapting to climate change. Developing science-based adaptation policies requires accurate coastal flood risk (CFR) assessments, which are often subject to the scarcity of sufficiently accurate input data for insular states. We analysed the impact of uncertain inputs on coastal flood damage estimates, considering: (i) significant wave height, (ii) storm surge level and (iii) sea level rise (SLR) contributions to extreme sea levels, as well as the error-driven uncertainty in (iv) bathymetric and (v) topographic datasets, (vi) damage models and (vii) socioeconomic changes. The methodology was tested through a sensitivity analysis using an ensemble of hydrodynamic models (XBeach and SFINCS) coupled with an impact model (Delft-FIAT) for a case study at the islands of São Tomé and Príncipe. Model results indicate that for the current time horizon, depth damage functions (DDF) and digital elevation model (DEM) dominate the overall damage estimation uncertainty. We find that, when introducing climate and socioeconomic uncertainties to the analysis, SLR projections become the most relevant input for the year 2100 (followed by DEM and DDF). In general, the scarcity of reliable input data leads to considerable predictive error in CFR assessments in SIDS. The findings of this research can help to prioritise the allocation of limited resources towards the acquisitions of the most relevant input data for reliable impact estimation.

scholarly journals The impact of the artery geometry with the sewn surgical patch on the blood flow disorders

2019 ◽  
Vol 128 ◽  
pp. 02005
Author(s):  
Natalia Lewandowska ◽  
Michal Cialkowski
Keyword(s):  
Blood Flow ◽  
Input Data ◽  
Polynomial Function ◽  
Blood Parameters ◽  
Atherosclerotic Plaques ◽  
Fluid Density ◽  
The Common ◽  
The Individual ◽  
The Impact ◽  
Patch Length

The research concerns the development of geometric variants of patches sewn into the common carotid artery during surgery of the atherosclerotic plaques removal. Based on analytical methods, thegeometry of the patch described by the polynomial function has been developed. The simulations of blood flow in the arteries with the sewn patch were performed. The study included the influence of the patient’s diameter and the width of the chosen patch on blood flow disorders. The result of the research is the algorithm of selecting the geometry of the arterial patch to the individual geometrical featuresof the patient’s artery. The studies result will comprise the development of software, which, upon introduction of input data related to arterial geometry, patch length and patient’s blood parameters (affecting the fluid density and viscosity), shall generate an accurate contour of the patch of width causing no flow disorders.

scholarly journals How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique

2019 ◽  
Vol 4 (Suppl 5) ◽  
pp. e000894
Author(s):  
Yolisa Prudence Dube ◽  
Corrine Warren Ruktanonchai ◽  
Charfudin Sacoor ◽  
Andrew J Tatem ◽  
Khatia Munguambe ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Input Data ◽  
Census Data ◽  
Healthcare Services ◽  
Perinatal Health ◽  
Prediction Errors ◽  
The Sustainable Development ◽  
Administrative Unit ◽  
Development Goals ◽  
The Impact

BackgroundExistence of inequalities in quality and access to healthcare services at subnational levels has been identified despite a decline in maternal and perinatal mortality rates at national levels, leading to the need to investigate such conditions using geographical analysis. The need to assess the accuracy of global demographic distribution datasets at all subnational levels arises from the current emphasis on subnational monitoring of maternal and perinatal health progress, by the new targets stated in the Sustainable Development Goals.MethodsThe analysis involved comparison of four models generated using Worldpop methods, incorporating region-specific input data, as measured through the Community Level Intervention for Pre-eclampsia (CLIP) project. Normalised root mean square error was used to determine and compare the models’ prediction errors at different administrative unit levels.ResultsThe models’ prediction errors are lower at higher administrative unit levels. All datasets showed the same pattern for both the live birth and pregnancy estimates. The effect of improving spatial resolution and accuracy of input data was more prominent at higher administrative unit levels.ConclusionThe validation successfully highlighted the impact of spatial resolution and accuracy of maternal and perinatal health data in modelling estimates of pregnancies and live births. There is a need for more data collection techniques that conduct comprehensive censuses like the CLIP project. It is also imperative for such projects to take advantage of the power of mapping tools at their disposal to fill the gaps in the availability of datasets for populated areas.

scholarly journals Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3322 ◽  
Author(s):  
Marieline Senave ◽  
Staf Roels ◽  
Stijn Verbeke ◽  
Evi Lambie ◽  
Dirk Saelens
Keyword(s):  
Sensitivity Analysis ◽  
Steady State ◽  
Data Analysis ◽  
Heat Loss ◽  
Input Data ◽  
Loss Coefficient ◽  
Wide Range ◽  
Board Monitoring ◽  
The Impact

Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

scholarly journals Modelling African biomass burning emissions and the effect of spatial resolution

2019 ◽  
Author(s):  
Dave van Wees ◽  
Guido R. van der Werf
Keyword(s):  
Spatial Resolution ◽  
Input Data ◽  
Large Scale ◽  
Sub Saharan Africa ◽  
Fuel Load ◽  
Burned Area ◽  
Fire Emissions ◽  
Continental Scale ◽  
Coarse Resolution ◽  
The Impact

Abstract. Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. In this paper, we developed a model using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500-meter spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050°, 0.125°, 0.250°). We estimated fire emissions of 0.68 PgC yr−1 at 500-meter resolution and 0.82 PgC yr−1 at 0.25° resolution; a difference of 24 %. At 0.25° resolution, our model results were relatively similar to GFED4, which also runs at 0.25° resolution, whereas our 500-meter estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 PgC yr−1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500-meter and 0.25° resolution simulations, besides those stemming from representation errors in the calibration process, namely: 1. biome misclassification leading to errors in parameterization, 2. errors due to the averaging of input data and the associated reduction in variability, and 3. a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations up to a factor 4, and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse resolution models and suggest the need for increased spatial resolution in global fire emission models.

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