In this paper, I discuss four different areas of my research. One portion of my research has focused on automatic synthesis of search control heuristics for constraint satisfaction problems (CSPs). I have developed techniques for automatically synthesizing two types of heuristics for CSPs: Filtering functions are used to remove portions of a search space from consideration. Another portion of my research is focused on automatic synthesis of hierarchic algorithms for solving constraint satisfaction problems (CSPs). I have developed a technique for constructing hierarchic problem solvers based on numeric interval algebra. Another portion of my research is focused on automatic decomposition of design optimization problems. We are using the design of racing yacht hulls as a testbed domain for this research. Decomposition is especially important in the design of complex physical shapes such as yacht hulls. Another portion of my research is focused on intelligent model selection in design optimization. The model selection problem results from the difficulty of using exact models to analyze the performance of candidate designs.
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This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.
We have undertaken a search for light echo signals from Supernova 1987A that have been serendipitously recorded in images taken near the 30 Doradus region of the Large Magellanic Cloud by HST. We used the MAST interface to create a database of the 1282 WF/PC, WFPC2 and STIS images taken within 15 arcminutes of the supernova, between 1992 April and 2002 June. These 1282 images are grouped into 125 distinct epochs and pointings, with each epoch containing between 1 and 42 separate exposures. Sorting this database with various programs, aided by the STScI Visual Target Tuner, we have identified 63 pairs of WFPC2 imaging epochs that are not centered on the supernova but that have a significant amount of spatial overlap between their fields of view. These image data were downloaded from the public archive, cleaned of cosmic rays, and blinked to search for light echoes at radii larger than 2 arcminutes from the supernova. Our search to date has focused on those pairs of epochs with the largest degree of overlap. Of 16 pairs of epochs scanned to date, we have detected 3 strong light echoes and one faint, tentative echo signal. We will present direct and difference images of these and any further echoes, as well as the 3-D geometric, photometric and color properties of the echoing dust structures. In addition, a set of 20 epochs of WF/PC and WFPC2 imaging centered on SN 1987A remain to be searched for echoes within 2 arcminutes of the supernova. We will discuss our plans to integrate the high spatial-resolution HST snapshots of the echoes with our extensive, well-time-sampled, ground-based imaging data. We gratefully acknowledge the support of this undergraduate research project through an HST Archival Research Grant (HST-AR-09209.01-A).
The physical and temporal systematics of the world's volcanic activity is a compelling and productive arena for the exercise of orbital remote sensing techniques, informing studies ranging from basic volcanology to societal risk. Comprised of over 160,000 frames and spanning 15 years of the Terra platform mission, the ASTER Volcano Archive (AVA: ) is the world's largest (100+Tb) high spatial resolution (15-30-90m/pixel), multi-spectral (visible-SWIR-TIR), downloadable (kml enabled) dedicated archive of volcano imagery. We will discuss the development of the AVA, and describe its growing capability to provide new easy public access to ASTER global volcano remote sensing data. AVA system architecture is designed to facilitate parameter-based data mining, and for the implementation of archive-wide data analysis algorithms. Such search and analysis capabilities exploit AVA's unprecedented time-series data compilations for over 1,550 volcanoes worldwide (Smithsonian Holocene catalog). Results include thermal anomaly detection and mapping, as well as detection of SO2 plumes from explosive eruptions and passive SO2 emissions confined to the troposphere. We are also implementing retrospective ASTER image retrievals based on volcanic activity reports from Volcanic Ash Advisory Centers (VAACs) and the US Air Force Weather Agency (AFWA). A major planned expansion of the AVA is currently underway, with the ingest of the full 1972-present LANDSAT, and NASA EO-1, volcano imagery for comparison and integration with ASTER data. Work described here is carried out under contract to NASA at the Jet Propulsion Laboratory as part of the California Institute of Technology.
One of the most expensive aspects of archiving digital documents is the manual acquisition of context-sensitive metadata useful for the subsequent discovery of, and access to, the archived items. For certain types of textual documents, such as journal articles, pamphlets, official government records, etc., where the metadata is contained within the body of the documents, a cost effective method is to identify and extract the metadata in an automated way, applying machine learning and string pattern search techniques.At the U. S. National Library of Medicine (NLM) we have developed an automated metadata extraction (AME) system that employs layout classification and recognition models with a metadata pattern search model for a text corpus with structured or semi-structured information. A combination of Support Vector Machine and Hidden Markov Model is used to create the layout recognition models from a training set of the corpus, following which a rule-based metadata search model is used to extract the embedded metadata by analyzing the string patterns within and surrounding each field in the recognized layouts.In this paper, we describe the design of our AME system, with focus on the metadata search model. We present the extraction results for a historic collection from the Food and Drug Administration, and outline how the system may be adapted for similar collections. Finally, we discuss some ongoing enhancements to our AME system.
One of the most expensive aspects of archiving digital documents is the manual acquisition of context-sensitive metadata useful for the subsequent discovery of, and access to, the archived items. For certain types of textual documents, such as journal articles, pamphlets, official government records, etc., where the metadata is contained within the body of the documents, a cost effective method is to identify and extract the metadata in an automated way, applying machine learning and string pattern search techniques. At the U. S. National Library of Medicine (NLM) we have developed an automated metadata extraction (AME) system that employs layout classification and recognition models with a metadata pattern search model for a text corpus with structured or semi-structured information. A combination of Support Vector Machine and Hidden Markov Model is used to create the layout recognition models from a training set of the corpus, following which a rule-based metadata search model is used to extract the embedded metadata by analyzing the string patterns within and surrounding each field in the recognized layouts. In this paper, we describe the design of our AME system, with focus on the metadata search model. We present the extraction results for a historic collection from the Food and Drug Administration, and outline how the system may be adapted for similar collections. Finally, we discuss some ongoing enhancements to our AME system. PMID:21179386
An architecture for the control of an autonomous aircraft is presented. The architecture is a hierarchical system representing an anthropomorphic breakdown of the control problem into planner, navigator, and pilot systems. The planner system determines high level global plans from overall mission objectives. This abstract mission planning is investigated by focusing on the Traveling Salesman Problem with variations on local and global constraints. Tree search techniques are applied including the breadth first, depth first, and best first algorithms. The minimum-column and row entries for the Traveling Salesman Problem cost matrix provides a powerful heuristic to guide these search techniques. Mission planning subgoals are directed from the planner to the navigator for planning routes in mountainous terrain with threats. Terrain/threat information is abstracted into a graph of possible paths for which graph searches are performed. It is shown that paths can be well represented by a search graph based on the Voronoi diagram of points representing the vertices of mountain boundaries. A comparison of Dijkstra's dynamic programming algorithm and the A* graph search algorithm from artificial intelligence/operations research is performed for several navigation path planning examples. These examples illustrate paths that minimize a combination of distance and exposure to threats. Finally, the pilot system synthesizes the flight trajectory by creating the control commands to fly the aircraft. 2ff7e9595c
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