scholarly journals Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1630
Author(s):  
Jane Jean Kiam ◽  
Eva Besada-Portas ◽  
Axel Schulte
Keyword(s):  
High Altitude ◽  
Weather Conditions ◽  
Mission Planning ◽  
Weather Data ◽  
Mission Success ◽  
Resolution Imaging ◽  
Solar Powered ◽  
High Level ◽  
Imaging Sensors ◽  
The Cost

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.

scholarly journals Reduced-Order Modeling of Stratospheric Winds and Its Application in High-Altitude Balloon Trajectory Simulations

2017 ◽  
Vol 56 (6) ◽  
pp. 1753-1766 ◽  
Author(s):  
Sai Sudha Ramesh ◽  
Kian Meng Lim ◽  
Heow Pueh Lee ◽  
Boo Cheong Khoo
Keyword(s):  
High Altitude ◽  
Weather Conditions ◽  
Temporal Variations ◽  
Weather Data ◽  
Compact Representation ◽  
Station Keeping ◽  
Quasi Biennial Oscillation ◽  
Wind Profiles ◽  
Fourier Model ◽  
Trajectory Simulations

AbstractThe knowledge of weather conditions at the stratosphere is important for the planning and execution of high-altitude balloon flights, which require an accurate modeling of weather data over a period of time. Various methods based on statistical analysis, artificial neural networks, and cluster analysis have been employed to model the temporal variation of weather parameters. In the present study, a proper orthogonal decomposition (POD) method has been used to study the spatial as well as temporal variations of wind data in Singapore. The use of POD facilitates a compact representation of the weather dataset and aids in faster computation of wind profiles for use in balloon trajectory simulation. Further, the results reveal the existence of the quasi-biennial oscillation phenomenon, which is characteristic of equatorial easterly–westerly winds. This phenomenon enables the development of a Fourier prediction model, which can be used in real-time balloon trajectory simulations. The Fourier model is observed to be sensitive to wind velocity fluctuations, especially in the vicinity of alternating wind directions. However, it provides a reasonable projection of balloon trajectory, which can be used in preliminary planning and testing of high-altitude flights. Thus, a prior knowledge of wind profiles based on POD or a Fourier model aids in balloon station keeping. A simple case of altitude-controlled balloon flight is presented, and the results highlight the advantages of the present method in balloon station keeping.

scholarly journals Addressing the Impact of Industrialized Components on the Cost of Temporary Heating in Cold-climate Regions

2018 ◽  
Author(s):  
Regina Dias Ferreira ◽  
Beda Barkokebas ◽  
Lana Secchi ◽  
Mustafa Gul ◽  
YuXiang Chen ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Residential Building ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Building Envelope ◽  
Cold Climate ◽  
Weather Data ◽  
The Cost ◽  
Construction Schedule ◽  
The Impact

In countries with cold climates such as Canada, the cost of providing space heating during the construction phase, also known as temporary heating, results in a significant additional construction cost, which causes budget deviations thus affecting the projectäó»s financial performance. In fact, the estimation of temporary heating is commonly overlooked due to the uncertainties such as weather forecast and the projectäó»s actual onsite schedule. The cost of temporary heating comprises two parts: (1) the cost of equipment rental, and (2) the fuel consumption required to heat a given area when the temperature falls below a certain threshold. The fuel consumption of the equipment is related to the temperature and exposure of the buildingäó»s envelope to the current weather conditions. Thus, the construction of the building envelope is critical to the reduction of fuel consumption and the consequent temporary heating cost of the project. In this context, the research presented in this paper aims to estimate the impacts of temporary heating for various constructive methods, such as the traditional stick-built practice and a few variations of panelized construction (in regard to the insulation used), by developing a simulation model to observe the variation of weather data, construction schedule, and fuel consumption for each scenario. To perform this analysis, a 4-story residential building located in the city of Edmonton, Alberta, Canada, is used as a case study in which the proposed scenarios are compared in order to address the advantages of industrialized components in reducing the cost of temporary heating.

Changing Role of Agriculture in Income and Employment, and Trends of Agricultural Worker Productivity in Indian States

2020 ◽  
pp. 183-189
Keyword(s):  
Green Revolution ◽  
Positive Trend ◽  
Worker Productivity ◽  
Indian States ◽  
Agricultural Worker ◽  
The Sixties ◽  
High Level ◽  
The Cost ◽  
Changing Role

The productivity of land has been often discussed and deliberated by the academia and policymakers to understand agriculture, however, very few studies have focused on the agriculture worker productivity to analyze this sector. This study concentrates on the productivity of agricultural workers from across the states taking two-time points into consideration. The agriculture worker productivity needs to be dealt with seriously and on a time series basis so that the marginal productivity of worker can be ascertained but also the dependency of worker on agriculture gets revealed. There is still disguised unemployment in all the states and high level of labour migration, yet most of the states showed the dependency has gone down. Although a state like Madhya Pradesh is doing very well in terms of income earned but that is at the cost of increased worker power in agriculture as a result of which, the productivity of worker has gone down. States like Mizoram, Meghalaya, Nagaland and Tripura, though small in size showed remarkable growth in productivity and all these states showed a positive trend in terms of worker shifting away from agriculture. The traditional states which gained the most from Green Revolution of the sixties are performing decently well, but they need to have the next major policy push so that they move to the next orbit of growth.

scholarly journals UAVs fleet mission planning robust to changing weather conditions

2020 ◽  
Vol 53 (2) ◽  
pp. 10518-10524
Author(s):  
Grzegorz Bocewicz ◽  
Grzegorz Radzki ◽  
Izabela Nielsen ◽  
Marcin Witczak ◽  
Banaszak Zbigniew
Keyword(s):  
Weather Conditions ◽  
Mission Planning

Erbium/Ytterbium-Doped Waveguide Amplifier (EYDWA) for extended reach of Wavelength Division Multiplexing based free space optics system (WDM/FSO)

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Bentahar Attaouia ◽  
Kandouci Malika ◽  
Ghouali Samir
Keyword(s):  
Wavelength Division Multiplexing ◽  
Free Space ◽  
Cost Effective ◽  
Weather Conditions ◽  
Free Space Optical ◽  
Wavelength Division ◽  
Space Optics ◽  
Effective Implementation ◽  
Transmission Distance ◽  
The Cost

AbstractThis work is focused to carry out the investigation of wavelength division multiplexing (WDM) approach on free space optical (FSO) transmission systems using Erbium Ytterbium Doped Waveguide Amplifier (EYDWA) integrated as post-or pre-amplifier for extending the reach to 30 Km for the cost-effective implementation of FSO system considering weather conditions. Furthermore, the performance of proposed FSO-wavelength division multiplexing (WDM) system is also evaluated on the effect of varying the FSO range and results are reported in terms of Q factor, BER, and eye diagrams. It has been found that, under clear rain the post-amplification was performed and was able to reach transmission distance over 27 Km, whereas, the FSO distance has been limited at 19.5 Km by using pre-amplification.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

Robust Environmental Sensing Using UAVs

2021 ◽  
Vol 2 (4) ◽  
pp. 1-20
Author(s):  
Ahmed Boubrima ◽  
Edward W. Knightly
Keyword(s):  
Air Pollution ◽  
Gas Sensors ◽  
High Performance ◽  
Weather Conditions ◽  
Ambient Air ◽  
Mission Planning ◽  
Pollution Monitoring ◽  
Compound Pollution ◽  
Planning Algorithm

In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.

scholarly journals Determining the critical period for weed control in high-yielding cotton using common sunflower as a mimic weed

2019 ◽  
Vol 33 (6) ◽  
pp. 800-807 ◽  
Author(s):  
Graham W. Charles ◽  
Brian M. Sindel ◽  
Annette L. Cowie ◽  
Oliver G. G. Knox
Keyword(s):  
Weed Control ◽  
Critical Period ◽  
Yield Loss ◽  
Field Studies ◽  
Growing Degree Days ◽  
Degree Days ◽  
Weed Density ◽  
High Level ◽  
The Cost ◽  
Planting Season

AbstractField studies were conducted over six seasons to determine the critical period for weed control (CPWC) in high-yielding cotton, using common sunflower as a mimic weed. Common sunflower was planted with or after cotton emergence at densities of 1, 2, 5, 10, 20, and 50 plants m−2. Common sunflower was added and removed at approximately 0, 150, 300, 450, 600, 750, and 900 growing degree days (GDD) after planting. Season-long interference resulted in no harvestable cotton at densities of five or more common sunflower plants m−2. High levels of intraspecific and interspecific competition occurred at the highest weed densities, with increases in weed biomass and reductions in crop yield not proportional to the changes in weed density. Using a 5% yield-loss threshold, the CPWC extended from 43 to 615 GDD, and 20 to 1,512 GDD for one and 50 common sunflower plants m−2, respectively. These results highlight the high level of weed control required in high-yielding cotton to ensure crop losses do not exceed the cost of control.

scholarly journals Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
David Vint ◽  
Matthew Anderson ◽  
Yuhao Yang ◽  
Christos Ilioudis ◽  
Gaetano Di Caterina ◽  
...  
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Sar Images ◽  
Adversarial Networks ◽  
Technological Advances ◽  
Dataset Size ◽  
Resolution Imaging ◽  
High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

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