共查询到20条相似文献,搜索用时 29 毫秒
1.
In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence
of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in
families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology
we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls
to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction
with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls
in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities
show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness
performances efficiently.
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2.
Clustering of time series subsequence data commonly produces results that are unspecific to the data set. This paper introduces
a clustering algorithm, that creates clusters exclusively from those subsequences that occur more frequently in a data set
than would be expected by random chance. As such, it partially adopts a pattern mining perspective into clustering. When subsequences
are being labeled based on such clusters, they may remain without label. In fact, if the clustering was done on an unrelated
time series it is expected that the subsequences should not receive a label. We show that pattern-based clusters are indeed
specific to the data set for 7 out of 10 real-world sets we tested, and for window-lengths up to 128 time points. While kernel-density-based
clustering can be used to find clusters with similar properties for window sizes of 8–16 time points, its performance degrades
fast for increasing window sizes.
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3.
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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4.
This paper presents a neural technique for simulating deformable objects. For the sake of achieving real-time and reducing
the computational time costs, the fuzzy Kohonen clustering network (FKCN) is used for modeling and visualization. However,
determining the physical parameters of differential equation is very difficult and sometimes different equation should be
solved to produce animations, which become ill-conditioned. The finite element model is normally very time-consuming, since
an appropriate structure must be defined. The proposed method can be more easily implemented than the finite element method.
The simulation results show that the neural network method produced plausible animation results.
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5.
Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for
exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying
the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation
that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective
feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation
and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite
effectively navigate complicated routes.
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6.
The artificial neural network (ANN) methodology has been used in various time series prediction applications. However, the accuracy of a neural network model may be seriously compromised when it is used recursively for making long-term multi-step predictions. This study presents a method using multiple ANNs to make a long term time series prediction. A multiple neural network (MNN) model is a group of neural networks that work together to solve a problem. In the proposed MNN approach, each component neural network makes forecasts at a different length of time ahead. The MNN method was applied to the problem of forecasting an hourly customer demand for gas at a compression station in Saskatchewan, Canada. The results showed that a MNN model performed better than a single ANN model for long term prediction. 相似文献
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.
Documents and authors can be clustered into “knowledge communities” based on the overlap in the papers they cite. We introduce
a new clustering algorithm, Streemer, which finds cohesive foreground clusters embedded in a diffuse background, and use it
to identify knowledge communities as foreground clusters of papers which share common citations. To analyze the evolution
of these communities over time, we build predictive models with features based on the citation structure, the vocabulary of
the papers, and the affiliations and prestige of the authors. Findings include that scientific knowledge communities tend
to grow more rapidly if their publications build on diverse information and if they use a narrow vocabulary.
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9.
A multiresolution-based bilinear recurrent neural network (MBLRNN) is proposed in this paper. The proposed MBLRNN is based
on the BLRNN that has robust abilities in modeling and predicting time series. The learning process is further improved by
using a multiresolution-based learning algorithm for training the BLRNN so as to make it more robust for the prediction of
time series data. The proposed MBLRNN is applied to the problems of network traffic prediction and electric load forecasting.
Experiments and results on both practical problems show that the proposed MBLRNN outperforms both the traditional multilayer
perceptron type neural network (MLPNN) and the BLRNN in the prediction accuracy.
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10.
The paper reflects on the unique experience of social and technological development in Lithuania since the regaining of independence
as a newly reshaped society constructing a distinctive competitive IST-based model at global level. This has presented Lithuanian
pattern of how to integrate different experiences and relations between generations in implementing complex information society
approaches. The resulting programme in general is linked to the Lisbon objectives of the European Union. The experience of
transitional countries in Europe, each different but facing some common problems, may be useful to developing countries in
Africa.
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11.
Research on hybrid bionic systems (HBSs) is still in its infancy but promising results have already been achieved in laboratories.
Experiments on humans and animals show that artificial devices can be controlled by neural signals. These results suggest
that HBS technologies can be employed to restore sensorimotor functionalities in disabled and elderly people. At the same
time, HBS research raises ethical concerns related to possible exogenous and endogenous limitations to human autonomy and
freedom. The analysis of these concerns requires reflecting on the availability of scientific models accounting for key aspects
of sensorimotor coordination and plastic adaptation mechanisms in the brain.
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12.
The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability
of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing
support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole.
A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular
scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards
increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings
partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable
behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.
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14.
Awareness systems have attracted significant research interest for their potential to support interpersonal relationships.
Investigations of awareness systems for the domestic environment have suggested that such systems can help individuals stay
in touch with dear friends or family and provide affective benefits to their users. Our research provides empirical evidence
to refine and substantiate such suggestions. We report our experience with designing and evaluating the ASTRA awareness system,
for connecting households and mobile family members. We introduce the concept of connectedness and its measurement through
the Affective Benefits and Costs of communication questionnaire (ABC-Q). We inform results that testify the benefits of sharing
experiences at the moment they happen without interrupting potential receivers. Finally, we document the role that lightweight,
picture-based communication can play in the range of communication media available.
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15.
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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16.
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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17.
The European Union co-funded COMUNICAR (communication multimedia unit inside car) project designed and developed an integrated multimedia human–machine interface (HMI) able to manage a wide variety of driver information systems (from entertainment to safety). COMUNICAR proposed an innovative information provision paradigm, in which the on-vehicle HMI is able to tailor the delivery of the information in real time according to the actual driving context and the drivers workload. COMUNICAR adopted a user-centred design process involving an iterative development based on extensive user tests since the early phases of the project. This approach was particularly useful to define and improve the layout of the user interface and specify the rules that decide the scheduling and the modalities of the delivery of the information messages to the driver. This paper introduces the COMUNICAR concept and the user-centred flow of design. Then, a concrete case of user-test driven, iterative improvement of a systems functionality is presented. We also briefly describe two software tools that we have designed to enhance the development process in a user-centred perspective. Finally, the future evolution of the concept of smart and safe information scheduling is sketched and discussed. 相似文献
18.
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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19.
Wealth distribution based on classic sugarscape model leads to a population increase and the Gini coefficient decrease when
cooperation and communication parameters are taken into account. In another study, this model was developed by implying a
receipt of one-fifth of the assets of the population and derived utilization for poor people. The results showed a relation
between mortality decrease, population increase, and Gini coefficient decrease (equality increase). In a synergic process,
the wealth adjustment based on sugarscape model underwent some experiments by implying communication and cooperation, and
the mechanism of receiving and utilizing the assets. The results show that the population increase and the Gini coefficient
decrease play an important role in wealth adjustment.
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20.
Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these
algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive
information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one
to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods,
instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving
clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general
features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better
protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated.
It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well
as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to
which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements.
In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then,
we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations
about future work and promising directions in the context of privacy preservation in data mining are discussed.
*The work reported in this paper has been partially supported by the EU under the IST Project CODMINE and by the Sponsors of
CERIAS.
Editor: Geoff Webb
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