441 research outputs found
04021 Abstracts Collection – Content-Based Retrieval
No full textFrom 04.01.04 to 09.01.04, the Dagstuhl Seminar 04021 ``Content-Based Retrieval''
was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
MAGELLAN: Map Acquisition of Geographic Labels by Legend Analysis
No full textthis paper, we describe a system called MAGELLAN (denoting Map Acquisition of GEographic Labels by Legend ANalysis) built by us that uses the legend of the map to drive the geographic symbol (or label) recognition for this purpose. MAGELLAN first locates the legend of the map and segments it. The geographic symbols are identified, and their semantic meaning is attached. An initial training set library is constructed based on this information. The training set library is subsequently used to classify geographic symbols in input maps using statistical pattern recognition [6]. User interaction is required at first to assist in constructing the training set library to account for variability in the symbols. Users may also change some of the classifier parameters at this stage to suit their specific application. Subsequently, MAGELLAN proceeds to automatically identify the geographic symbols in the input maps that use the same legend. The emphasis of our approach is on utilizing the legend of the map to build a flexible and adaptive system that extracts symbolic information from map layers, 2 Hanan Samet, Aya Soffer: MAGELLAN: Map Acquisition of Geographic Labels by Legend Analysis rather than trying to vectorize the entire map and interpret every object found in it (some of these objects may not appear in the legend). The goal of MAGELLAN is to extract contextual cues from the map layer that can be used to index the composite maps in a map image information system. The input to MAGELLAN are raster images of the separate map layers. The output of MAGELLAN is a logical representation of a map image that that can be used by a map image information system to automatically index both the composite and layer images. See [22] for a description of a companion system called MARCO ..
Automatic generation of robot and manual assembly plans using octrees
No full textThis paper aims to investigate automatic assembly planning for robot and manual assembly. The octree decomposition technique is applied to approximate CAD models with an octree representation which are then used to generate robot and manual assembly plans. An assembly planning system able to generate assembly plans was developed to build these prototype models. Octree decomposition is an effective assembly planning tool. Assembly plans can automatically be generated for robot and manual assembly using octree models. Research limitations/implications - One disadvantage of the octree decomposition technique is that it approximates a part model with cubes instead of using the actual model. This limits its use and applications when complex assemblies must be planned, but in the context of prototyping can allow a rough component to be formed which can later be finished by hand. Assembly plans can be generated using octree decomposition, however, new algorithms must be developed to overcome its limitations
Pseudo Picard Operators On Generalized Metric Spaces
No full textAltun, Ishak/0000-0002-7967-0554; Samet, Bessem/0000-0002-6769-3417In this paper, we present a new class of pseudo Picard operators in the setting of generalized metric spaces introduced recently in [M. JLELI AND B. SAMET: A generalized metric space and related fixed point theorems, Fixed Point Theory Appl., (2015) 2015:61]. An example is provided to illustrate the main result.Deanship of Scientific Research at King Saud UniversityDeanship of Scientific Research at King Saud University [RGP-237]The second author extends his appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No RGP-237
AIRSPACE PLANNING FOR OPTIMAL CAPACITY, EFFICIENCY, AND SAFETY USING ANALYTICS
No full textAir Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method for planning optimal airspace capacity, efficiency, and safety. These goals require separation and sequencing of aircraft before they depart. Prior approaches have tactically achieved these goals to some extent. However, dealing with increasingly congested airspace and new environmental factors with high levels of uncertainty still remains the challenge when deterministic approach is used. Hence due to the nature of uncertainties, we take a stochastic approach and propose a suite of analytics models for (1) Flight Time Prediction, (2) Aircraft Trajectory Clustering, (3) Aircraft Trajectory Prediction, and (4) Aircraft Conflict Detection and Resolution long before aircraft depart. The suite of data-driven models runs on a scalable Data Management System that continuously processes streaming massive flight data to achieve the strategic airspace planning for optimal capacity, efficiency, and safety.
(1) Flight Time Prediction. Unlike other systems that collect and use features only for the arrival airport to build a data-driven model for predicting flight times, we use a richer set of features along the potential route, such as weather parameters and air traffic data in addition to those that are particular to the arrival airport. Our feature engineering process generates an extensive set of multidimensional time series data which goes through Time Series Clustering with Dynamic Time Warping (DTW) to generate a single set of representative features at each time instance. The features are fed into various regression and deep learning models and the best performing models with most accurate ETA predictions are selected. Evaluations on extensive set of real trajectory, weather, and airport data in Europe verify our prediction system generates more accurate ETAs with far less variance than those of European ANSP, EUROCONTROL’s. This translates to more accurately predicted flight arrival times, enabling airlines to make more cost-effective ground resource allocation and ANSPs to make more efficient flight scheduling.
(2) Aircraft Trajectory Clustering. The novel divide-cluster-merge; DICLERGE system clusters aircraft trajectories by dividing them into the three standard major flight phases: climb, en-route, and descent. Trajectory segments in each phase are clustered in isolation, then merged together. Our unique approach also discovers a representative trajectory, the model for the entire trajectory set.
(3) Aircraft Trajectory Prediction. Our approach considers airspace as a 3D grid network, where each grid point is a location of a weather observation. We hypothetically build cubes around these grid points, so the entire airspace can be considered as a set of cubes. Each cube is defined by its centroid, the original grid point, and associated weather parameters that remain homogeneous within the cube during a period of time. Then, we align raw trajectories to a set of cube centroids which are basically fixed 3D positions independent of trajectory data. This creates a new form of trajectories which are 4D joint cubes, where each cube is a segment that is associated with not only spatio-temporal attributes but also with weather parameters. Next, we exploit machine learning techniques to train inference models from historical data and apply a stochastic model, a Hidden Markov Model (HMM), to predict trajectories taking environmental uncertainties into account. During the process, we apply time series clustering to generate input observations from an excessive set of weather parameters to feed into the Viterbi algorithm. The experiments use a real trajectory dataset with pertaining weather observations and demonstrate the effectiveness of our approach to the trajectory prediction process for Air Traffic Management.
(4) Aircraft Conflict Detection. We propose a novel data-driven system to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the system declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The system resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the system learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the system avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict-free. The key concept upon which the system is built is the assumption that the airspace is nothing more than a horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate the system using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR.
Overall, in this thesis, we develop a suite of analytics models and algorithms to accurately identify current patterns in the massive flight data and use these patterns to predict future behaviors in the airspace. Upon prediction of a non-ideal outcome, we prescribe a solution to plan airspace for optimal capacity, efficiency, and safety
Out-of-core multiresolution terrain modeling
No full textIn this chapter we discuss issues about level of detail (LOD) representations for digital terrain models and, especially, we describe how to deal with very large terrain data sets through out-of-core techniques that explicitly manage I/O operations between levels of memory. LOD modeling in the related context of geographical maps is discussed in Chaps. 4 and 5. A data set describing a terrain consists of a set of elevation measurements taken at a finite number of locations over a planar or a spherical domain. In a digital terrain model, elevation is extended to the whole domain of interest by averaging or interpolating the available measurements. Of course, the resulting model is affected by some approximation error, and, in general, the higher the density of the samples, the smaller the error. The same arguments can be used for more general two-dimensional scalar or vector fields (e.g. generated by simulation), defined over a manifold domain, and measured through some sampling process. Available terrain data sets are becoming larger and larger, and processing them at their full resolution often exhibits prohibitive computational costs, even for high-end workstations. Simplification algorithms and multiresolution models proposed in the literature may improve efficiency, by adapting resolution on-the-fly, according to the needs of a specific application [32]. Data at high resolution are preprocessed once to build a multiresolutionmodel that can be queried online by the application. The multiresolution model acts as a black box that provides simplified representations, where resolution is focused on the region of interest and at the LOD required by the application. A simplified representation is generally affected by some approximation error that is usually associated with either the vertices or the cells of the simplified mesh. Since current data sets often exceed the size of the main memory, I/O operations between levels of memory are often the bottleneck in computation. A disk access is about one million times slower than an access to main memory.A naive management of external memory, for example, with standard caching and virtualmemory policies, may thus highly degrade the algorithm performance. Indeed, some computations are inherently non-local and require large numbers of I/O operations. Out-of-core 44 Emanuele Danovaro, Leila De Floriani, Enrico Puppo, and Hanan Samet algorithms and data structures explicitly control how data are loaded and how they are stored. Here, we review methods and models proposed in the literature for simplification and multiresolution management of huge datasets that cannot be handled in main memory.We consider methods that are suitable to manage terrain data, some of which have been developed for more general kinds of data (e.g. triangle meshes describing the boundary of 3D objects). The rest of this chapter is organized as follows. In Sect. 3.2, we introduce the necessary background about digital terrain models, focusing our attention on triangulated irregular networks (TINs). In Sect. 3.3, we review out-of-core techniques for simplification of triangle meshes and discuss their application to terrain data to produce approximated representations. In Sect. 3.4, we review out-of-core multiresolution models specific for regularly distributed data, while in Sect. 3.5, we describe more general out-of-core multiresolution models that can manage irregularly distributed data. In Sect. 3.6, we draw some conclusions and discuss open research issues including extensions to out-of-core simplification and multiresolution modeling of scalar fields in three and higher dimensions, to deal, for instance, with geological data. © Springer-Verlag Berlin Heidelberg 2007. All rights are reserved
Meir-Keeler Type Contractions on Js-Metric Spaces and Related Fixed Point Theorems
No full textKARAPINAR, ERDAL/0000-0002-6798-3254We introduce two classes of Meir-Keeler type contractions in the framework of JS-metric spaces introduced by Jleli and Samet (2015). For each class, a fixed point result is derived. Some interesting consequences which follow from our obtained results are discussed.King Saud University (Saudi Arabia)The second author extends his appreciation to Distinguished Scientist Fellowship Program (DSFP) at King Saud University (Saudi Arabia)
Espor : Türkiye’de alternatif hayran araştırması
No full textCataloged from PDF version of article.Includes bibliographical references (leaves 76-80).A fan can be anyone who has both potential media consumer and producer. Fandom
as we call, is a community of fans interested in a specific media context such as
actor, author or TV series. This study is about the developing fandom around
eSports (electronic sports) in Turkey, analyzing game fans interaction with League of
Legends which has become a product of popular culture. To investigate eSports
fandom, this study relies on interviews with professional and amateur players and
virtual ethnographic methods. Findings of the interviews and ethnographic data aim
to ground the similarities between League of Legends players reproducing and code
switching techniques in the light of Anglo American studies on series and movie
fandom.by Samet Taygun Özbıçakçı
Proximity queries on terrain surface
No full textDue to the advance of the geo-spatial positioning and the computer graphics technology, digital terrain data has become increasingly popular nowadays. Query processing on terrain data has attracted considerable attention from both the academic and the industry communities. Proximity queries such as the shortest path/distance query, k nearest/farthest neighbor query, and top-k closest/farthest pairs query are fundamental and important queries in the context of the terrain surfaces, and they have a lot of applications in Geographical Information System, 3D object feature vector construction, and 3D object data mining. In this article, we first study the most fundamental type of query, namely, shortest distance and path query, which is to find the shortest distance and path between two points of interest on the surface of the terrain. As observed by existing studies, computing the exact shortest distance/path is very expensive. Some existing studies proposed ϵ-approximate distance and path oracles, where ϵ is a non-negative real-valued error parameter. However, the best-known algorithm has a large oracle construction time, a large oracle size, and a large query time. Motivated by this, we propose a novel ϵ-approximate distance and path oracle called the Space Efficient distance and path oracle (SE), which has a small oracle construction time, a small oracle size, and a small distance and path query time, thanks to its compactness of storing concise information about pairwise distances between any two points-of-interest. Then, we propose several algorithms for the k nearest/farthest neighbor and top-k closest/farthest pairs queries with the assistance of our distance and path oracle SE. Our experimental results show that the oracle construction time, the oracle size, and the distance and path query time of SE are up to two, three, and five orders of magnitude faster than the best-known algorithm, respectively. Besides, our algorithms for other proximity queries including k nearest/farthest neighbor queries and top-k closest/farthest pairs queries significantly outperform the state-of-the-art algorithms by up to two orders of magnitude.Ministry of Education (MOE)The research of Victor Junqiu Wei is supported by PolyU internal fund (1-BD47) under the research project (P0039657) of the Hong Kong Polytechnic University. The research of the HKUST side is supported by PRP/026/21FX. This research of Cheng Long is supported in part by the Ministry of Education, Singapore, under its Academic Research Fund (Tier 2 Awards MOE-T2EP20220-0011 and MOE-T2EP20221-0013). The research of David Mount was supported by NSF grant CCF-1618866. The research of Hanan Samet was sponsored in part by the NSF under Grants IIS-18-16889, IIS-20-41415, and IIS-21-14451
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