A Hierarchical Approach to Periodic Scheduling of Large Scale Traffic Light Systems

1991 ◽  
pp. 199-209
Author(s):  
C. Pascolo ◽  
P. Serafini ◽  
W. Ukovich
Keyword(s):  
Large Scale ◽  
Hierarchical Approach ◽  
Traffic Light ◽  
Periodic Scheduling

Divide and conquer: A hierarchical approach to large-scale structure-from-motion

2017 ◽  
Vol 157 ◽  
pp. 190-205 ◽  
Author(s):  
Brojeshwar Bhowmick ◽  
Suvam Patra ◽  
Avishek Chatterjee ◽  
Venu Madhav Govindu ◽  
Subhashis Banerjee
Keyword(s):  
Structure From Motion ◽  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Divide And Conquer ◽  
Hierarchical Approach

The DWD climate prediction website

2021 ◽  
Author(s):  
Andreas Paxian ◽  
Katja Reinhardt ◽  
Birgit Mannig ◽  
Katharina Isensee ◽  
Amelie Krug ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Prediction ◽  
Climate Projections ◽  
Climate Data ◽  
Traffic Light ◽  
Daily Work ◽  
Bias Drift ◽  
Climate Service ◽  
German Cities ◽  
Decadal Predictions

<p>DWD provides operational seasonal and decadal predictions of the German climate prediction system since 2016 and 2020, respectively. We plan to present these predictions together with post-processed ECMWF sub-seasonal forecast products on the DWD climate prediction website www.dwd.de/climatepredictions. In March 2020, this climate service was published with decadal predictions for the coming years; sub-seasonal and seasonal predictions for the coming weeks and months will follow.</p><p>The user-oriented evaluation and design of this climate service has been developed in close cooperation with users from various sectors at workshops of the German MiKlip project and will be consistent across all time scales. The website offers maps, time series and tables of ensemble mean and probabilistic predictions in combination with the prediction skill for 1-year and 5-year means/ sums of temperature and precipitation for different regions (World, Europe, Germany, German regions).</p><p>For Germany, the statistical downscaling EPISODES was applied to reach high spatial resolution required by several climate data users. Decadal predictions were statistically recalibrated in order to adjust bias, drift and standard deviation and optimize ensemble spread. We used the MSESS and RPSS to evaluate the skill of climate predictions in comparison to reference predictions, e.g. ‘observed climatology’ or ‘uninitialized climate projections’ (which are both applied by users until now as an alternative to climate predictions). The significance was tested via bootstraps.</p><p>Within the ‘basic climate predictions’ section, a user-oriented traffic light indicates whether regional-mean climate predictions are significantly better (green), not significantly different (yellow) or significantly worse (red) than reference predictions. Within the ‘expert climate predictions’ section, prediction maps show per grid box the prediction itself (via the color of dots) and its skill (via the size of dots representing the skill categories of the traffic light). The co-development of this climate prediction application with users from different sectors strongly improves the comprehensibility and applicability by users in their daily work.</p><p>In addition to sub-seasonal and seasonal predictions, plans for future extensions of this climate service include multi-year seasonal predictions, e.g. 5-year summer or winter means, combined products for climate predictions and climate projections, further user-oriented, extreme or large-scale variables, e.g. ENSO, or high-resolution applications for German cities based on statistically downscaled predictions.</p>

scholarly journals Preliminary establishment and analysis of automated driving benchmark in China

2019 ◽  
Vol 259 ◽  
pp. 02002
Author(s):  
Xiong Hui ◽  
Yinghan Wang ◽  
Qiang Ge ◽  
Ziqing Gu ◽  
Mingyang Cui ◽  
...  
Keyword(s):  
Large Scale ◽  
Road User ◽  
Future Application ◽  
Area Network ◽  
Traffic Light ◽  
Automated Driving ◽  
Traffic Sign ◽  
Traffic Characteristics ◽  
Chinese Characteristic ◽  
And Control

In order to promote the localization of Automated Driving (AD) in China, it is necessary to collect large-scale traffic scene data with Chinese characteristic for future analysis. In this paper, we propose the methodologies and rules of establishing AD benchmark involving how to configure sensors, how to design the collection schema to show Chinese traffic characteristics and the rules of elaborating distinctive scenes and routes, what to label, and it is also demonstrated that the benchmark can support the future application of extended AD research. Data collection lasted about one month covering diverse scene data such as campus, highway, park, etc. from three representative Chinese cities and driving data from 30 different drivers. Moreover, some statistical results and analyses are produced in accordance with the designed methodologies as instances of potential application. Up to now, the dataset contains about 7,000 labelled image frames and corresponding LiDAR, GPS and Controller Area Network (CAN) data. Labels cover scene type, road user, traffic sign, traffic light, and lane marker. This benchmark can help researchers better understand Chinese traffic situation in aspects of environmental perception, driving behavior analysis, risk assessment, automated vehicle decision and control.

scholarly journals A Hierarchical Approach to Protein Fold Prediction

2011 ◽  
Vol 8 (1) ◽  
pp. 66-77 ◽  
Author(s):  
Tabrez Anwar Shamim Mohammad ◽  
Hampapathalu Adimurthy Nagarajaram
Keyword(s):  
Prediction Accuracy ◽  
Large Scale ◽  
Fold Recognition ◽  
Classification Problem ◽  
New Method ◽  
Support Vector ◽  
Protein Fold ◽  
Hierarchical Approach ◽  
Structural Class ◽  
Fold Prediction

Summary Fold recognition, assigning novel proteins to known structures, forms an important component of the overall protein structure discovery process. The available methods for protein fold recognition are limited by the low fold-coverage and/or low prediction accuracies. We describe here a new Support Vector Machine (SVM)-based method for protein fold prediction with high prediction accuracy and high fold-coverage. The new method of fold prediction with high fold-coverage was developed by training and testing on a large number of folds in order to make the method suitable for large scale fold predictions. However, presence of large number of folds in the training set made the classification task difficult as a consequence of increased complexity involved in binary classifications of SVMs. In order to overcome this complexity we adopted a hierarchical approach where fold-prediction is made in two steps. At the first step structural class of the query is predicted and at the second step fold is predicted within the predicted structural class. This decreased the complexity of the classification problem and also improved the overall fold prediction accuracy. To the best of our knowledge this is the first taxonomic fold recognition method to cover over 700 protein-folds and gives prediction accuracy of around 70% on a benchmark dataset. Since the new method gives rise to state of the art prediction performance and hence can be very useful for structural characterization of proteins discovered in various genomes.

scholarly journals Slave to the rhythm: how large-scale climate cycles trigger herring (Clupea harengus) regeneration in the North Sea

2009 ◽  
Vol 67 (3) ◽  
pp. 454-465 ◽  
Author(s):  
Joachim P. Gröger ◽  
Gordon H. Kruse ◽  
Norbert Rohlf
Keyword(s):  
North Sea ◽  
North Atlantic ◽  
Large Scale ◽  
Clupea Harengus ◽  
Traffic Light ◽  
The North ◽  
Climate Cycles ◽  
Stock Assessments ◽  
The North Sea ◽  
The North Atlantic

AbstractGröger, J. P., Kruse, G. H., and Rohlf, N. 2010. Slave to the rhythm: how large-scale climate cycles trigger herring (Clupea harengus) regeneration in the North Sea. – ICES Journal of Marine Science, 67: 454–465. Understanding the causes of variability in the recruitment of marine fish stocks has been the “holy grail” of fisheries scientists for more than 100 years. Currently, debate is ongoing about the functionality and performance of traditional stock–recruitment functions used during stock assessments. Additionally, the European Commission requires European fishery scientists to apply the ecosystem approach to fisheries in part by integrating environmental knowledge into stock assessments and forecasts. Motivated to understand better the recent years of reproductive failures of commercially valuable North Sea herring, we studied large-scale climate changes in the North Atlantic Ocean and their potential effects on stock regeneration. Applying traffic light plots and time-series (TS) analyses, it was possible not only to explain the most recent reproductive failures, but also to reconstruct the full TS of recruitment from climate cycles, indexed by the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation. A prognostic model was developed to provide predictions of herring stock changes several years in advance, allowing recruitment forecasts to be incorporated easily into risk assessments and management strategy evaluations, to promote a sustainable herring fishery in the North Sea. Insights gained from the analysis permit reinterpretation of the sharp decline in the North Sea herring stocks in the 1970s.

Sign in / Sign up

Export Citation Format

Share Document