Refinements to a Procedure for Estimating Airfield Capacity

2015 ◽  
Vol 2501 (1) ◽  
pp. 18-24 ◽  
Author(s):  
Amy Kim ◽  
S. A. Rokib ◽  
Yi Liu
Keyword(s):  
Performance Metrics ◽  
Historical Data ◽  
Meteorological Condition ◽  
Data Sets ◽  
Data Set ◽  
International Airport ◽  
Air Traffic Controllers ◽  
Ground Delay ◽  
Censored Regression Model ◽  
Airport Runway

This paper presents a method for obtaining airfield capacity estimates using historical data from FAA's Aviation System Performance Metrics (ASPM) database. The process first involves merging individual flights and quarter-hour airport runway operations data sets from ASPM to create a new data set. Data for Newark Liberty International Airport (EWR) in New Jersey and San Diego International Airport in California from 2006 to 2011 were used. Then, filters for meteorological condition, runway configuration, called rates, and fleet mix were applied to the two airport data sets. The filtered data sets were then used in a censored regression model of capacity that included queue length (number of aircraft waiting to arrive or depart) and arrival–departure throughput count splits as independent variables. These attributes were found to affect airfield capacity at statistically significant levels, and parameters had expected signs and magnitudes. Additionally, capacities under ideal conditions were found to be reasonably close to other sources. The model also confirmed that average capacities at EWR during hours when a ground delay program (GDP) was running were lower than when there was no GDP in effect. The method described in this paper could be used to more precisely quantify airfield capacities in specific conditions of particular interest to air traffic controllers and airport operators to better facilitate decisions that rely heavily on a good understanding of capacity in these conditions. The data exploration and preparation undertaken as part of the study reveal some of the finer points of the ASPM data and how they can be used in a more meaningful way for airfield capacity estimation.

Defusing Technology:Technology Diffusion in British Columbia

1993 ◽  
Vol 9 (1) ◽  
pp. 46-61 ◽  
Author(s):  
Arminée Kazanjian ◽  
Kathryn Friesen
Keyword(s):  
British Columbia ◽  
Historical Data ◽  
Current Data ◽  
Fiscal Year ◽  
Data Sets ◽  
Canadian Province ◽  
Data Set ◽  
Time Periods ◽  
Institutional Profile ◽  
Areas Of Interest

AbstractIn order to explore the diffusion of the selected technologies in one Canadian province (British Columbia), two administrative data sets were analyzed. The data included over 40 million payment records for each fiscal year on medical services provided to British Columbia residents (2,968,769 in 1988) and information on physical facilities, services, and personnel from 138 hospitals in the province. Three specific time periods were examined in each data set, starting with 1979–80 and ending with the most current data available at the time. The detailed retrospective analysis of laboratory and imaging technologies provides historical data in three areas of interest: (a) patterns of diffusion and volume of utilization, (b) institutional profile, and (c) provider profile. The framework for the analysis focused, where possible, on the examination of determinants of diffusion that may be amenable to policy influence.

COMPARATIVE ANALYSIS OF PREDICTION TECHNIQUES ON THE BASIS OF TELECOM CUSTOMER CHURN

2021 ◽  
Vol 16 (9) ◽  
Author(s):  
Yasser Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Broadband Service ◽  
Customer Churn ◽  
Artificial Neural ◽  
Prediction Techniques

Telecommunication customer churn is considered as major cause for dropped revenue and customer baseline of voice, multimedia and broadband service provider. There is strong need on focusing to understand the contributory factors of churn. Now considering factors from data sets obtained from Pakistan major telecom operators are applied for modeling. On the basis of results obtained from the optimal techniques, comparative technical evaluation is carried out. This research study is comprised mainly of proposition of conceptual frame work for telecom customer churn that lead to creation of predictive model. This is trained tested and evaluated on given data set taken from Pakistan Telecom industry that has provided accurate & reliable outcomes. Out of four prevailing statistical and machine learning algorithm, artificial neural network is declared the most reliable model, followed by decision tree. The logistic regression is placed at last position by considering the performance metrics like accuracy, recall, precision and ROC curve. The results from research has revealed main parameters found responsible for customer churn were data rate, call failure rate, mean time to repair and monthly billing amount. On the basis of these parameter artificial neural network has achieved 79% more efficiency as compare to low performing statistical techniques.

How to evaluate rainfall estimation performance? - A discussion of metrics, thresholds and aggregations for one year of country-wide CML rainfall estimation

2020 ◽  
Author(s):  
Maximilian Graf ◽  
Christian Chwala ◽  
Julius Polz ◽  
Harald Kunstmann
Keyword(s):  
Performance Metrics ◽  
Temporal Aggregation ◽  
Data Sets ◽  
Rainfall Estimation ◽  
Data Set ◽  
Data Source ◽  
One Year ◽  
Point Line ◽  
Rain Rates ◽  
The Impact

<p>In recent years, so-called opportunistic sensors for measuring rainfall, are attracting more notice due to their broad availability and low financial effort for the scientific community. These sensors are existing devices or infrastructure, which were not intentionally built to measure rainfall, but can deliver rainfall information. One example of such an opportunistic measurement system are Commercial Microwave Links (CMLs), which provide part of the backbone of modern mobile communication. CMLs can deliver path-averaged rainfall information through the relation between rainfall and attenuation along their paths. Before such an opportunistic data source can be used, either as an individual or a merged data product, its performance compared to other rainfall products must be evaluated.</p><p>We discuss the selection of performance metrics, spatial and temporal aggregation and rainfall thresholds for the comparison between a German-wide CML network and a gauge-adjusted radar product provided by the German Weather Service. The CML data set consists of nearly 4000 CMLs with minutely readings from which we will present a year of data. </p><p>First, we show the influence of the temporal aggregation on the comparability. With higher resolution, the impact due to small temporal deviations increases. Second, CMLs represent path-averaged rainfall information, while the radar product is gridded. We discuss the choice whether the comparison should be performed on the point, line or grid scale. This choice depends on the desired future applications which already should be considered when selection evaluation tools. Third, the decision to exclude rain rates below a certain threshold or the calculation of performance metrics for certain intervals gives us a more detailed insight in the behavior of both rainfall data sets.</p>

scholarly journals Research and Appalication of Software Defect Predictionn based on BP-Migration learning

2018 ◽  
Vol 232 ◽  
pp. 03017
Author(s):  
Jie Zhang ◽  
Gang Wang ◽  
Haobo Jiang ◽  
Fangzheng Zhao ◽  
Guilin Tian
Keyword(s):  
Prediction Model ◽  
Historical Data ◽  
Defect Prediction ◽  
Software Project ◽  
Data Sets ◽  
Software Defect Prediction ◽  
Software Module ◽  
Data Set ◽  
Software Defect ◽  
Project Data

Software Defect Prediction has been an important part of Software engineering research since the 1970s. This technique is used to calculate and analyze the measurement and defect information of the historical software module to complete the defect prediction of the new software module. Currently, most software defect prediction model is established on the basis of the same software project data set. The training date sets used to construct the model and the test data sets used to validate the model are from the same software projects. But in practice, for those has less historical data of a software project or new projects, the defect of traditional prediction method shows lower forecast performance. For the traditional method, when the historical data is insufficient, the software defect prediction model cannot be fully studied. It is difficult to achieve high prediction accuracy. In the process of cross-project prediction, the problem that we will faced is data distribution differences. For the above problems, this paper presents a software defect prediction model based on migration learning and traditional software defect prediction model. This model uses the existing project data sets to predict software defects across projects. The main work of this article includes: 1) Data preprocessing. This section includes data feature correlation analysis, noise reduction and so on, which effectively avoids the interference of over-fitting problem and noise data on prediction results. 2) Migrate learning. This section analyzes two different but related project data sets and reduces the impact of data distribution differences. 3) Artificial neural networks. According to class imbalance problems of the data set, using artificial neural network and dynamic selection training samples reduce the influence of prediction results because of the positive and negative samples data. The data set of the Relink project and AEEEM is studied to evaluate the performance of the f-measure and the ROC curve and AUC calculation. Experiments show that the model has high predictive performance.

scholarly journals Constraints of using historical data for modelling the spatial distribution of helminth parasites in ruminants

Parasite ◽  
2021 ◽  
Vol 28 ◽  
pp. 46
Author(s):  
Alizée Hendrickx ◽  
Cedric Marsboom ◽  
Laura Rinaldi ◽  
Hannah Rose Vineer ◽  
Maria Elena Morgoglione ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Sample Size ◽  
Species Distribution ◽  
Historical Data ◽  
Data Sets ◽  
Stable Model ◽  
Helminth Parasites ◽  
Distribution Models ◽  
Baseline Model ◽  
Data Set

Dicrocoelium dendriticum is a trematode that infects ruminant livestock and requires two different intermediate hosts to complete its lifecycle. Modelling the spatial distribution of this parasite can help to improve its management in higher risk regions. The aim of this research was to assess the constraints of using historical data sets when modelling the spatial distribution of helminth parasites in ruminants. A parasitological data set provided by CREMOPAR (Napoli, Italy) and covering most of Italy was used in this paper. A baseline model (Random Forest, VECMAP®) using the entire data set was first used to determine the minimal number of data points needed to build a stable model. Then, annual distribution models were computed and compared with the baseline model. The best prediction rate and statistical output were obtained for 2012 and the worst for 2016, even though the sample size of the former was significantly smaller than the latter. We discuss how this may be explained by the fact that in 2012, the samples were more evenly geographically distributed, whilst in 2016 most of the data were strongly clustered. It is concluded that the spatial distribution of the input data appears to be more important than the actual sample size when computing species distribution models. This is often a major issue when using historical data to develop spatial models. Such data sets often include sampling biases and large geographical gaps. If this bias is not corrected, the spatial distribution model outputs may display the sampling effort rather than the real species distribution.

scholarly journals Assessing the completeness of Italian historical earthquake data

2009 ◽  
Vol 47 (2-3) ◽  
Author(s):  
M. Stucchi ◽  
P. Albini ◽  
M. Mirto ◽  
A. Rebez
Keyword(s):  
Historical Data ◽  
Seismic Hazard Assessment ◽  
Probabilistic Seismic Hazard Assessment ◽  
Data Sets ◽  
Historical Earthquake ◽  
Earthquake Catalogues ◽  
Data Set ◽  
Historical Methods ◽  
The Impact ◽  
Earthquake Data

The assessment of the completeness of historical earthquake data (such as, for instance, parametric earthquake catalogues) has usually been approached in seismology - and mainly in Probabilistic Seismic Hazard Assessment(PSHA) - by means of statistical procedures. Such procedures look «inside» the data set under investigation and compare it to seismicity models, which often require more or less explicitly that seismicity is stationary. They usually end up determining times (Ti), from which on the data set is considered as complete above a given magnitude (Mi); the part of the data set before Ti is considered as incomplete and, for that reason, not suitable for statistical analysis. As a consequence, significant portions of historical data sets are not used for PSHA. Dealing with historical data sets - which are incomplete by nature, although this does not mean that they are of low value - it seems more appropriate to estimate «how much incomplete» the data sets can be and to use them together with such estimates. In other words, it seems more appropriate to assess the completeness looking «outside » the data sets; that is, investigating the way historical records have been produced, preserved and retrieved. This paper presents the results of investigation carried out in Italy, according to historical methods. First, the completeness of eighteen site seismic histories has been investigated; then, from those results, the completeness of areal portions of the catalogue has been assessed and compared with similar results obtained by statistical methods. Finally, the impact of these results on PSHA is described.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals The model and simulation of low impact development of the sponge airport, China

2019 ◽  
Vol 20 (2) ◽  
pp. 383-394 ◽  
Author(s):  
Jing Peng ◽  
Jiayi Ouyang ◽  
Lei Yu ◽  
Xinchen Wu
Keyword(s):  
Flood Control ◽  
Low Impact Development ◽  
Water Environment ◽  
Drainage System ◽  
Practical Significance ◽  
International Airport ◽  
Model And Simulation ◽  
Impervious Area ◽  
Before And After ◽  
Airport Runway

Abstract Recently urban waterlogging problems have become more and more serious, and the construction of an airport runway makes the impervious area of the airport high, which leads to the deterioration of the water environment and frequent waterlogging disasters. It is of great significance to design and construct the sponge airport with low impact development (LID) facilities. In this paper, we take catchment N1 of Beijing Daxing International Airport as a case study. The LID facilities are designed and the runoff process of a heavy rainfall in catchment N1 is simulated before and after the implementation of LID facilities. The results show that the total amount of surface runoff, the number of overflow junctions and full-flow conduits of the rainwater drainage system in catchment N1 of Beijing Daxing International Airport are significantly reduced after the implementation of the LID facilities. Therefore, the application of LID facilities has greatly improved the ability of the airport to remove rainwater and effectively alleviated the risk of waterlogging in the airport flight area. This study provides theoretical support for airport designers and managers to solve flood control and rainwater drainage problems and has vital practical significance.

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