共查询到10条相似文献,搜索用时 125 毫秒
1.
Tracking location is challenging due to the numerous constraints of practical systems including, but not limited to global
cost, device volume and weight, scalability and accuracy; these constraints are typically more severe for systems that should
be wearable and used indoors. We investigate the use of wearable solar cells to track changing light conditions (a concept
that we named LuxTrace) as a source of user displacement and activity data. We evaluate constraints of this approach and present
results from an experimental validation of displacement and activity estimation. The results indicate that a distance estimation
accuracy of 21 cm (80% quantile) can be achieved. A simple method to combine LuxTrace with complementary absolute location
estimation methods is also presented. We apply carpet-like distributed RFID tags to demonstrate online learning of new lighting
environments.
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2.
We present a study of using camera-phones and visual-tags to access mobile services. Firstly, a user-experience study is described in which participants were both observed learning to interact with a prototype mobile service and interviewed
about their experiences. Secondly, a pointing-device task is presented in which quantitative data was gathered regarding the speed and accuracy with which participants aimed and clicked
on visual-tags using camera-phones. We found that participants’ attitudes to visual-tag-based applications were broadly positive,
although they had several important reservations about camera-phone technology more generally. Data from our pointing-device
task demonstrated that novice users were able to aim and click on visual-tags quickly (well under 3 s per pointing-device
trial on average) and accurately (almost all meeting our defined speed/accuracy tradeoff of 6% error-rate). Based on our findings,
design lessons for camera-phone and visual-tag applications are presented.
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3.
Backfitting of fuzzy rules is an Iterative Rule Learning technique for obtaining the knowledge base of a fuzzy rule-based
system in regression problems. It consists in fitting one fuzzy rule to the data, and replacing the whole training set by
the residual of the approximation. The obtained rule is added to the knowledge base, and the process is repeated until the
residual is zero, or near zero. Such a design has been extended to imprecise data for which the observation error is small.
Nevertheless, when this error is moderate or high, the learning can stop early. In this kind of algorithms, the specificity
of the residual might decrease when a new rule is added. There may happen that the residual grows so wide that it covers the
value zero for all points (thus the algorithm stops), but we have not yet extracted all the information available in the dataset.
Focusing on this problem, this paper is about datasets with medium to high discrepancies between the observed and the actual
values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality
of the iterative learning degrades in this kind of problems, because it does not make full use of all the available information.
As an alternative to sequentially obtaining rules, we propose a new multiobjective Genetic Cooperative Competitive Learning
(GCCL) algorithm. In our approach, each individual in the population codifies one rule, which competes in the population in
terms of maximum coverage and fitting, while the individuals in the population cooperate to form the knowledge base.
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4.
This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking
algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired
at the beginning of tracking. The tracking therefore could fail if the appearance of the object varies substantially. In this
paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance
can be approximated by piece-wise linear combination of these key appearances in histogram space. The generative process is
described by a Bayesian graphical model. An Online EM algorithm is proposed to estimate the model parameters from the observed
histogram in the tracking window and to update the appearance histogram. We applied this approach to track human head motion
and to infer the head pose simultaneously in videos. Experiments verify that our online histogram generative model constrained
by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the
enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our
tracking algorithm can infer additional information such as the object poses.
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.
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5.
In this paper, we present a fully Bayesian approach for generalized Dirichlet mixtures estimation and selection. The estimation
of the parameters is based on the Monte Carlo simulation technique of Gibbs sampling mixed with a Metropolis-Hastings step.
Also, we obtain a posterior distribution which is conjugate to a generalized Dirichlet likelihood. For the selection of the
number of clusters, we used the integrated likelihood. The performance of our Bayesian algorithm is tested and compared with
the maximum likelihood approach by the classification of several synthetic and real data sets. The generalized Dirichlet mixture
is also applied to the problems of IR eye modeling and introduced as a probabilistic kernel for Support Vector Machines.
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6.
To get the maximum benefit from ambient intelligence (AmI), we need to anticipate and react to possible drawbacks and threats
emerging from the new technologies in order to devise appropriate safeguards. The SWAMI project took a precautionary approach
in its exploration of the privacy risks in AmI and sought ways to reduce them. It constructed four “dark scenarios” showing
possible negative implications of AmI, notably for privacy protection. Legal analysis of the depicted futures showed the shortcomings
of the current legal framework in being able to provide adequate privacy protection in the AmI environment. In this paper,
the authors, building upon their involvement in SWAMI research as well as the further advancement of EU privacy analysis,
identify various outstanding issues regarding the legal framework that still need to be resolved in order to deal with AmI
in an equitable and efficacious way. This article points out some of the lacunae in the legal framework and postulates several
privacy-specific safeguards aimed at overcoming them.
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7.
Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed. 相似文献
8.
Nowadays data mining plays an important role in decision making. Since many organizations do not possess the in-house expertise
of data mining, it is beneficial to outsource data mining tasks to external service providers. However, most organizations
hesitate to do so due to the concern of loss of business intelligence and customer privacy. In this paper, we present a Bloom
filter based solution to enable organizations to outsource their tasks of mining association rules, at the same time, protect
their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off the
storage requirement.
This research was supported by the USA National Science Foundation Grants CCR-0310974 and IIS-0546027.
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9.
Ranking problems have recently become an important research topic in the joint field of machine learning and information retrieval.
This paper presented a new splitting rule that introduces a metric, i.e., an impurity measure, to construct decision trees
for ranking tasks. We provided a theoretical basis and some intuitive explanations for the splitting rule. Our approach is
also meaningful to collaborative filtering in the sense of dealing with categorical data and selecting relative features.
Some experiments were made to illustrate our ranking approach, whose results showed that our algorithm outperforms both perceptron-based
ranking and the classification tree algorithms in term of accuracy as well as speed.
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10.
This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary
Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition
and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational
intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process,
we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.
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