The advanced research version of the weather research and forecasting model was employed to simulate Intense Operational Periods of the field campaigns of the Mountain Terrain Atmospheric Modeling ...and Observations (MATERHORN) Program with a 0.5 km horizontal resolution. The focus was on synoptically dominated stably stratified periods, with the mean flow approaching a strongly non-symmetrical rugged topography—the Granite Mountain of the US Army Dugway Proving Ground, Utah. The model was validated against a comprehensive set of MATERHORN data. The special in-house developed software enabled calculations of energetics and pressure anomalies along individual streamlines and tracing of spatial trajectories of streamlines. Owing to complexities of natural flows, for example, the directional shear (skewed vertical velocity profiles) and irregular topographic shape, the flow patterns depend on multiple parameters, although in idealized cases the flow is described by a single parameter (Froude number or a variant). Streamlines at different altitudes of a given location diverged rapidly, making it difficult to study the dividing streamline concept. The new software allowed identification of the dividing streamline passing over the highest crest (summit) of the mountains and its trajectory. The upstream height of the dividing streamline did not follow the well-known Sheppard’s formula. Three cases of flow patterns are presented, identified based on the presence of lee waves, flow separation, horizontal vortex shedding and hydraulics jumps.
THE MATERHORN Fernando, H. J. S.; Pardyjak, E. R.; Di Sabatino, S. ...
Bulletin of the American Meteorological Society,
11/2015, Letnik:
96, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Emerging application areas such as air pollution in megacities, wind energy, urban security, and operation of unmanned aerial vehicles have intensified scientific and societal interest in mountain ...meteorology. To address scientific needs and help improve the prediction of mountain weather, the U.S. Department of Defense has funded a research effort—the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program—that draws the expertise of a multidisciplinary, multiinstitutional, and multinational group of researchers. The program has four principal thrusts, encompassing modeling, experimental, technology, and parameterization components, directed at diagnosing model deficiencies and critical knowledge gaps, conducting experimental studies, and developing tools for model improvements. The access to the Granite Mountain Atmospheric Sciences Testbed of the U.S. Army Dugway Proving Ground, as well as to a suite of conventional and novel high-end airborne and surface measurement platforms, has provided an unprecedented opportunity to investigate phenomena of time scales from a few seconds to a few days, covering spatial extents of tens of kilometers down to millimeters. This article provides an overview of the MATERHORN and a glimpse at its initial findings. Orographic forcing creates a multitude of time-dependent submesoscale phenomena that contribute to the variability of mountain weather at mesoscale. The nexus of predictions by mesoscale model ensembles and observations are described, identifying opportunities for further improvements in mountain weather forecasting.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
SUPPLEMENT Fernando, H. J. S.; Pardyjak, E. R.; Di Sabatino, S. ...
Bulletin of the American Meteorological Society,
11/2015, Letnik:
96, Številka:
11
Journal Article
Recenzirano
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
One of the missing critical on-board safety equipment of the Unmanned Arial Vehicles (UAVs) is the collision avoidance system. In 2010 we launched a project to research and develop an SAA system for ...UAS. As the system will be on-board in a small aircraft we have to minimize the weight, the volume, and the power consumption. The acceptable power consumption is 1-2W and the mass of the control system is maximum 300-500g. Here we present the concept of a visual input based See and Avoid (SAA) system. This paper introduces the long range visual detection algorithm and the implementation aspect of the many core processor device.
Visual sense-and-avoid system for UAVs Zarandy, A.; Zsedrovits, T.; Nagy, Z. ...
2012 13th International Workshop on Cellular Nanoscale Networks and their Applications,
2012-Aug.
Conference Proceeding
A visual sense-and-avoid system is introduced in this paper. The system is designed to operate on small and medium sized UAVs, and to be able to detect and avoid small manned and unmanned aircrafts. ...The intruder detection is done on a 4650×1280 sized video flow which is processed by a many-core cellular processor array real-time.
Cellular processor array based UAV safety system Zarandy, A.; Zsedrovits, T.; Nagy, Z. ...
2012 13th International Workshop on Cellular Nanoscale Networks and their Applications,
2012-Aug.
Conference Proceeding
Embedded sensor-processor system is being developed for on-board UAV (Unmanned Aerial Vehicle) safety applications. The role of the device is to detect intruder airplanes which are on or close to ...collision course. Due to weight, power, size, and cost requirements, the visual approach leads to feasible solution only. In our design, 5 cameras are applied to collect visual data from a large field of view. The image flows are processed by 3 different virtual cellular processor arrays, which are implemented in FPGA.
Visual detection based sense and avoid problem is more and more important nowadays as UAVs are getting closer to entering remotely piloted or autonomously into the airspace. It is critical to gain as ...much information as possible from the silhouettes of the distant aircrafts. In our paper, we investigate the reachable accuracy of the orientation information of remote planes under different geometrical condition, by identifying their wing lines from their detected wingtips. Under the assumption that the remote airplane is on a straight course, the error of the spatial discretization (pixelization), and the automatic detection error is calculated.
The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture ...integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor.
Open-set recognition models, in addition to generalizing to unseen instances of known categories, have to identify samples of unknown classes during the training phase. The main reason the latter is ...much more complicated is that there is very little or no information about the properties of these unknown classes. There are methodologies available to handle the unknowns. One possible method is to construct models for them by using generated inputs labeled as unknown. Generative adversarial networks are frequently deployed to generate synthetic samples representing unknown classes to create better models for known classes. In this paper, we introduce a novel approach to improve the accuracy of recognition methods while reducing the time complexity. Instead of generating synthetic input data to train neural networks, feature vectors are generated using the output of a hidden layer. This approach results in a less complex structure for the neural network representation of the classes. A distance-based classifier implemented by a convolutional neural network is used in our implementation. Our solution’s open-set detection performance reaches an AUC value of 0.839 on the CIFAR-10 dataset, while the closed-set accuracy is 91.4%, the highest among the open-set recognition methods. The generator and discriminator networks are much smaller when generating synthetic inner features. There is no need to run these samples through the first part of the classifier with the convolutional layers. Hence, this solution not only gives better performance than generating samples in the input space but also makes it less expensive in terms of computational complexity.
We introduce and analyze a fast horizon detection algorithm with native radial distortion handling and its implementation on a low power field programmable gate array (FPGA) development board in this ...paper. The algorithm is suited for visual applications in an airborne environment, that is on board a small unmanned aircraft. The algorithm was designed to have low complexity because of the power consumption requirements. To keep the computational cost low, an initial guess for the horizon is used, which is provided by the attitude heading reference system of the aircraft. The camera model takes radial distortions into account, which is necessary for a wide-angle lens used in most applications. This paper presents formulae for distorted horizon lines and a gradient sampling-based resolution-independent single shot algorithm for finding a horizon with radial distortion without undistortion of the complete image. The implemented algorithm is part of our visual sense-and-avoid system, where it is used for the sky-ground separation, and the performance of the algorithm is tested on real flight data. The FPGA implementation of the horizon detection method makes it possible to add this efficient module to any FPGA-based vision system.
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