Towards wake vortex safety and capacity increase: the integrated fusion approach and its demands on prediction models and detection sensors

2012 ◽  
Vol 227 (1) ◽  
pp. 199-208 ◽  
Author(s):  
Shanna Schönhals ◽  
Meiko Steen ◽  
Peter Hecker
Keyword(s):  
System Performance ◽  
Prediction Models ◽  
Air Transportation ◽  
Wake Vortex ◽  
Wake Turbulence ◽  
Air Space ◽  
High Level ◽  
Space Capacity ◽  
Error State ◽  
Fusion Approach

Wake vortices and the prevention of wake vortex encounters are both an issue of safety and capacity in today’s air transportation system. Current wake vortex separations are safe but also very conservative and thus have an adverse effect on capacity of airports. This article deals with the concept of fused wake vortex prediction and detection with the objective to deliver wake vortex strength and position with a high level of accuracy and reliability. The collaboration approach aims at fusion of models and sensors available for forecast and detection of hazardous wake turbulence in order to improve the overall system performance using the complementary capabilities of the single components. Different methods of coupling models with measurements are introduced and resulting the aspect of requirements for the prediction model and measurement sensor is presented. The implementation of an error-state system is presented and compared to sole prediction and sole sensor results. The results indicate that the fusion approach delivers benefits like reduced uncertainty of prediction and increased availability of detection and thus has the ability to increase airport and air space capacity while maintaining or even improving current wake vortex safety.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals Machine Learning–enabled Scalable Performance Prediction of Scientific Codes

2021 ◽  
Vol 31 (2) ◽  
pp. 1-28
Author(s):  
Gopinath Chennupati ◽  
Nandakishore Santhi ◽  
Phill Romero ◽  
Stephan Eidenbenz
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Prediction Models ◽  
Radiation Transport ◽  
Discrete Event ◽  
Basic Block ◽  
Distribution Models ◽  
Scientific Application ◽  
High Level ◽  
Access Patterns

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques

1998 ◽  
Vol 1635 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Wael H. Awad ◽  
Bruce N. Janson
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Linear Regression ◽  
Prediction Models ◽  
Washington State ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Training Process ◽  
Truck Accidents ◽  
High Level

Three different modeling approaches were applied to explain truck accidents at interchanges in Washington State during a 27-month period. Three models were developed for each ramp type including linear regression, neural networks, and a hybrid system using fuzzy logic and neural networks. The study showed that linear regression was able to predict accident frequencies that fell within one standard deviation from the overall mean of the dependent variable. However, the coefficient of determination was very low in all cases. The other two artificial intelligence (AI) approaches showed a high level of performance in identifying different patterns of accidents in the training data and presented a better fit when compared to the regression model. However, the ability of these AI models to predict test data that were not included in the training process showed unsatisfactory results.

Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count Prediction

2022 ◽  
Vol 40 (1) ◽  
pp. 1-29
Author(s):  
Siqing Li ◽  
Yaliang Li ◽  
Wayne Xin Zhao ◽  
Bolin Ding ◽  
Ji-Rong Wen
Keyword(s):  
Peer Review ◽  
Prediction Models ◽  
Citation Count ◽  
Specific Aspect ◽  
Semantic Features ◽  
Predictive Capacity ◽  
Topic Distribution ◽  
Real World Datasets ◽  
Data Signal ◽  
High Level

Citation count prediction is an important task for estimating the future impact of research papers. Most of the existing works utilize the information extracted from the paper itself. In this article, we focus on how to utilize another kind of useful data signal (i.e., peer review text) to improve both the performance and interpretability of the prediction models. Specially, we propose a novel aspect-aware capsule network for citation count prediction based on review text. It contains two major capsule layers, namely the feature capsule layer and the aspect capsule layer, with two different routing approaches, respectively. Feature capsules encode the local semantics from review sentences as the input of aspect capsule layer, whereas aspect capsules aim to capture high-level semantic features that will be served as final representations for prediction. Besides the predictive capacity, we also enhance the model interpretability with two strategies. First, we use the topic distribution of the review text to guide the learning of aspect capsules so that each aspect capsule can represent a specific aspect in the review. Then, we use the learned aspect capsules to generate readable text for explaining the predicted citation count. Extensive experiments on two real-world datasets have demonstrated the effectiveness of the proposed model in both performance and interpretability.

scholarly journals Identification of Aircraft Wake Vortex Based on SVM

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Weijun Pan ◽  
Zhengyuan Wu ◽  
Xiaolei Zhang
Keyword(s):  
Environmental Parameters ◽  
Classification Model ◽  
Support Vector ◽  
Wake Vortex ◽  
Doppler Lidar ◽  
Pulsed Doppler ◽  
Wind Fields ◽  
Wake Turbulence ◽  
Aircraft Wake ◽  
Separation Standards

The aircraft wake vortex has important influence on the operation of the airspace utilization ratio. Particularly, the identification of aircraft wake vortex using the pulsed Doppler lidar characteristics provides a new knowledge of wake turbulence separation standards. This paper develops an efficient pattern recognition-based method for identifying the aircraft wake vortex measured with the pulsed Doppler lidar. The proposed method is outlined in two stages. (i) First, a classification model based on support vector machine (SVM) is introduced to extract the radial velocity features in the wind fields by combining the environmental parameters. (ii) Then, grid search and cross-validation based on soft margin SVM with kernel tricks are employed to identify the aircraft wake vortex, using the test dataset. The dataset includes wake vortices of various aircrafts collected at the Chengdu Shuangliu International Airport from Aug 16, 2018, to Oct 10, 2018. The experimental results on dataset show that the proposed method can identify the aircraft wake vortex with only a small loss, which ensures the satisfactory robustness in detection performance.

Total System Performance Assessment Model for the Final Environmental Impact Statement for the Potential High-Level Nuclear Waste Repository at Yucca Mountain

2002 ◽  
Author(s):  
George J. Saulnier ◽  
K. Patrick Lee ◽  
Donald A. Kalinich ◽  
S. David Sevougian ◽  
Jerry A. McNeish
Keyword(s):  
System Performance ◽  
Transport Model ◽  
Yucca Mountain ◽  
Nuclear Waste Repository ◽  
Total System ◽  
Operating Modes ◽  
Final Rule ◽  
High Level Nuclear Waste ◽  
High Level ◽  
Component Models

The total-system performance assessment (TSPA) model for the final environmental impact statement (FEIS) for the potential high-level nuclear-waste repository at Yucca Mountain, Nevada was developed from a series of analyses and model studies of the Yucca Mountain site. The U.S. Department of Energy (DOE) has recommended the Yucca Mountain, Nevada site for the potential development of a geologic repository for the disposal of high-level radioactive waste and spent nuclear fuel. In May 2001, the DOE released the Yucca Mountain Science and Engineering Report (S&ER) for public review and comment. The S&ER summarizes more than 20 years of scientific and engineering studies supporting the site recommendation (SR). Following internal reviews of the S&ER and other documents, the DOE performed supplemental analyses of uncertainty in support of the SR as summarized in the Supplemental Science and Performance Analysis (SSPA) reports [2, 3]. The SSPA (1) provided insights into the impact of new scientific data and improved models and (2) evaluated a range of thermal operating modes and their effect on the predicted performance of a potential repository. The various updated component models for the SSPA resulted in a modified TSPA model, referred to as the supplemental TSPA model or SSPA TSPA model capturing the combined effects of the alternative model representations on system performance. The SSPA TSPA model was the basis for analyses for the FEIS for the Yucca Mountain site. However, after completion of the SSPA, the U.S. Environmental Protection Agency (EPA) released its final radiation-protection standards for the potential repository at Yucca Mountain (40 CFR Part 197). Compliance with the regulation required modification of several of the component models (e.g., the biosphere transport model and the saturated-zone transport model) in order to evaluate repository performance against the new standards. These changes were incorporated into the SSPA TSPA model. The resulting FEIS TSPA model, known as the “integrated TSPA model,” was used to perform the calculations presented in this report. The results of calculations using the FEIS TSPA model under a non-disruptive scenario, show that the potential disposal of commercial and DOE waste at a Yucca Mountain repository would not produce releases to the environment that would exceed the regulatory standards promulgated in the EPA Final Rule 10 CFR 197 and the NRC Final Rule 10 CFR 63 for both individual protection and groundwater protection. The analyses also show that both the high and low-temperature operating modes result in similar mean annual dose to the reasonably maximally exposed individual (RMEI). Further, the analyses show that consideration of intrusive and extrusive igneous events, human intrusion, or inclusion of the potential inventory of all radioactive material in the commercial and DOE inventory would not exceed those published standards.

scholarly journals Mining Deformation Life Cycle in the Light of InSAR and Deformation Models

2019 ◽  
Vol 11 (7) ◽  
pp. 745 ◽  
Author(s):  
Maya Ilieva ◽  
Piotr Polanin ◽  
Andrzej Borkowski ◽  
Piotr Gruchlik ◽  
Kamil Smolak ◽  
...  
Keyword(s):  
Life Cycle ◽  
Prediction Models ◽  
Time Lag ◽  
Model Parameters ◽  
Time Intervals ◽  
Deformation Velocity ◽  
Model Improvement ◽  
High Level ◽  
Deformation Models ◽  
Differential Radar Interferometry

The Sentinel-1 constellation provides an effective new radar instrument with a short revisit time of six days for the monitoring of intensive mining surface deformations. Our goal is to investigate in detail and to bring new comprehension of the mine life cycle. The dynamics of mining, especially in the case of horizontally evolving longwall technology, exhibit rapid surface changes. We use the classical approach of differential radar interferometry (DInSAR) with short temporal baselines (six days), which results in deformation maps with a low decorrelation between the satellite images. For the same time intervals, we compare the radar results with prediction models based on the Knothe–Budryk theory for mining subsidence. The validation of the results with ground levelling measurements reveals a high level of resemblance of the DInSAR subsidence maps (−0.04 m bias with respect to the levelling). On the other hand, aside from the explicable exaggeration, the location of the subsidence trough needs improvement in the forecasted deformations (0.2 km shift in location, a deformation velocity four times higher than in DInSAR). In addition, a time lag between DInSAR (compatible with extraction) and prediction is revealed. The model improvement can be achieved by including the DInSAR results in the elaboration of the model parameters.

On an analytical model of wake vortex separation of aircraft

2016 ◽  
Vol 120 (1232) ◽  
pp. 1534-1565 ◽  
Author(s):  
L.M.B.C. Campos ◽  
J.M.G. Marques
Keyword(s):  
Analytical Model ◽  
Simple Formula ◽  
Separation Distance ◽  
Civil Aviation ◽  
Wake Vortex ◽  
Atmospheric Conditions ◽  
International Civil Aviation Organization ◽  
Wake Turbulence ◽  
Vortex Separation ◽  
Light Medium

ABSTRACTA theory is presented on the effect of wake turbulence of a leading aircraft on the roll stability of a following aircraft, leading to a simple formula for the safe separation distance between the two aircraft that provides estimates of aircraft separation distances comparable to existing empirical regulations, based on experience. The formula includes the effects of flight and atmospheric conditions, and the characteristics of the leading and following aircraft; it applies to similar or dissimilar aircraft, and it indicates the parameters and conditions leading to increasing or decreasing separation. The formula is applied not only to the three International Civil Aviation Organization (ICAO) categories of aircraft (light, medium and heavy, respectively, Cessna Citation, B737 and B747) but also to ‘special’ aircraft requiring larger separation distance (Boeing 757) and to the world’s largest airliner (Airbus A380).

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