Sensing methods

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  • An increasingly common requirement of computer systems is to extract information regarding the people present in an environment. In this article "A Survey of Human-Sensing: Methods for Detecting Presence, Count, Location, Track, and Identity "we provide a comprehensive, multi-disciplinary survey of the existing literature, focusing mainly on the extraction of five commonly needed spatiotemporal properties: Namely presence, count, location, track and identity,...

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  • This book provides a wealth of new information, ideas and analysis on some of the key unknowns in hurricane research.

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  • A java application can run inside a JVM and can only invoke the methods of the classes available inside this JVM • Distributed computing or processing resolves around clientserver technology where several client programs communicate with one or more server applications.An RMI application has to expose methods, which remote clients can invoke. • These methods which are meant to be remote, should be defined in an interface which extends the java.rmi.Remote interface

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  • The development of robust accuracy assessment methods for the validation of spatial data represents a difficult challenge for the geospatial science community. The importance and timeliness of this issue are related directly to the dramatic escalation in the development and application of spatial data throughout the latter 20th century. This trend, which is expected to continue, will become increasingly pervasive and continue to revolutionize future decision-making processes.

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  • In this book, Applications of Monte Carlo Method in Science and Engineering, we further expose the broad range of applications of Monte Carlo simulation in the fields of Quantum Physics, Statistical Physics, Reliability, Medical Physics, Polycrystalline Materials, Ising Model, Chemistry, Agriculture, Food Processing, X-ray Imaging, Electron Dynamics in Doped Semiconductors, Metallurgy, Remote Sensing and much more diverse topics. The book chapters included in this volume clearly reflect the current scientific importance of Monte Carlo techniques in various fields of research....

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  • The study of economics should begin with a sense of wonder. Pause for a moment and consider a typical day in your life. It might start with a bagel made in a local bakery with flour produced in Minnesota from wheat grown in Kansas and bacon from pigs raised in Ohio packaged in plastic made in New Jersey. You spill coffee from Colombia on your shirt made in Texas from textiles shipped from South Carolina.

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  • In order to evaluate the status of damage caused by the stresses on the plants growing process before visible damage is noticed, we have followed the evolution of the Chl content in the plant leaves using the Fr/FFr chlorophyll fluorescence ratio. The absolute emission signal of leaves can vary from sample to sample due to small differences such as excitation and sensing angles of the fluorescence, and the roughness and scattering properties of the leaf surface. Thus, the absolute fluorescence usually varies to a large extent than the fluorescence ratio.

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  • Tham khảo sách 'applications of monte carlo method in sciencapplications of monte carlo method in science and engineering_1e and engineering_1', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • The importance of particle characterization in both the research and development, and the manufacture and quality control of materials and products that we use in our everyday lives is, in some sense, invisible to those of us not directly involved with these activities. Few of us know how particle size, shape, or surface characteristics can influence, for example, the efficacy of a painreliever, or the efficiency of a catalytic converter, or the resolution of a printer.

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  • This paper focuses on domain-specific senses and presents a method for assigning category/domain label to each sense of words in a dictionary. The method first identifies each sense of a word in the dictionary to its corresponding category. We used a text classification technique to select appropriate senses for each domain. Then, senses were scored by computing the rank scores. We used Markov Random Walk (MRW) model. The method was tested on English and Japanese resources, WordNet 3.0 and EDR Japanese dictionary. ...

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  • Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguation. In this paper, we present a method for reducing the granularity of the WordNet sense inventory based on the mapping to a manually crafted dictionary encoding sense hierarchies, namely the Oxford Dictionary of English. We assess the quality of the mapping and the induced clustering, and evaluate the performance of coarse WSD systems in the Senseval-3 English all-words task.

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  • In this paper a novel solution to automatic and unsupervised word sense induction (WSI) is introduced. It represents an instantiation of the ‘one sense per collocation’ observation (Gale et al., 1992). Like most existing approaches it utilizes clustering of word co-occurrences. This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs. The combination with a two-step clustering process using sentence co-occurrences as features allows for accurate results.

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  • This paper describes unsupervised learning algorithm for disambiguating verbal word senses using term weight learning. In our method, collocations which characterise every sense are extracted using similarity-based estimation. For the results, term weight learning is performed. Parameters of term weighting are then estimated so as to maximise the collocations which characterise every sense and minimise the other collocations. The resuits of experiment demonstrate the effectiveness of the method. ...

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  • This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Although most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full-fledged lexical ambiguity resolution should combine several information sources and techniques. The set of techniques have been applied in a combined way to disambiguate the genus terms of two machine-readable dictionaries (MRD), enabling us to construct complete taxonomies for Spanish and French. ...

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  • We compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency. The similarity-based methods perform up to 40% better on this particular task. We also conclude that events that occur only once in the training set have major impact on similarity-based estimates.

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  • This paper proposes to solve the bottleneck of finding training data for word sense disambiguation (WSD) in the domain of web queries, where a complete set of ambiguous word senses are unknown. In this paper, we present a combination of active learning and semi-supervised learning method to treat the case when positive examples, which have an expected word sense in web search result, are only given. The novelty of our approach is to use “pseudo negative examples” with reliable confidence score estimated by a classifier trained with positive and unlabeled examples.

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  • Selecting the most appropriate sense for an ambiguous word in a sentence is a central problem in Natural Language Processing. In this paper, we present a method that attempts to disambiguate all the nouns, verbs, adverbs and adjectives in a text, using the senses provided in WordNet.

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  • We describe a method for obtaining subject-dependent word sets relative to some (subjecO domain. Using the subject classifications given in the machine-readable version of Longman's Dictionary of Contemporary English, we established subject-dependent cooccurrence links between words of the defining vocabulary to construct these "neighborhoods". Here, we describe the application of these neighborhoods to information retrieval, and present a method of word sense disambiguation based on these co-occurrences, an extension of previous work. ...

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  • We describe a statistical technique for assigning senses to words. An instance of a word is assigned a sense by asking a question about the context in which the word appears. The question is constructed to have high mutual information with the translation of that instance in another language. When we incorporated this method of assigning senses into our statistical machine translation system, the error rate of the system decreased by thirteen percent. language model does not realize that take my own decision is improbable because take and decision no longer fall within a single trigram. ...

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  • The degree of dominance of a sense of a word is the proportion of occurrences of that sense in text. We propose four new methods to accurately determine word sense dominance using raw text and a published thesaurus. Unlike the McCarthy et al. (2004) system, these methods can be used on relatively small target texts, without the need for a similarly-sensedistributed auxiliary text. We perform an extensive evaluation using artificially generated thesaurus-sense-tagged data.

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