Software product line (SPL) engineering is one approach for addressing customization and variability for products. However, current approaches and research, while often addressing feature modeling and component variab...
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Software product line (SPL) engineering is one approach for addressing customization and variability for products. However, current approaches and research, while often addressing feature modeling and component variability, insufficiently address difficulties and additional complexity with respect to entity model variability, which negatively impacts various software qualities, such as correctness, reusability, maintainability, testability, and evolvability. This paper presents a solution approach with an integrated mechanism providing a consistent view to capture data variability in entity models, while hiding and decoupling components from superfluous data elements via adapter generation. An eHealth SPL case study is presented supporting adapter generation with differential entity conversion. The results show that with this approach, entity model variability can be effectively addressed and desirable software qualities preserved.
The design of computerarchitectures is a very complex problem. The multiple parameters make the number of possible combinations extremely *** researchers have used simulation, although it is a slow solution since eva...
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The design of computerarchitectures is a very complex problem. The multiple parameters make the number of possible combinations extremely *** researchers have used simulation, although it is a slow solution since evaluating a single point of the search space can take hours. In this work we propose using evolutionary multilayer perceptron (MLP) to compute the performance of an architecture parameter settings. Instead of exploring the search space, simulating many configurations, our method randomly selects some architecture configurations; those are simulated to obtain their performance, and then an artificial neural network is trained to predict the remaining configurations performance. Results obtained show a high accuracy of the estimations using a simple method to select the configurations we have to simulate to optimize the MLP. In order to explore the search space, we have designed a genetic algorithm that uses the MLP as fitness function to find the niche where the best architecture configurations (those with higher performance) are located. Our models need only a small fraction of the design space, obtaining small errors and reducing required simulation by two orders of magnitude.
Anomaly detection can detect unknown or new intrusions, and there are increasing interest in it. A normal network behavior model is necessary in any anomaly detection. Assuming that abnormal network behaviors are obvi...
ISBN:
(纸本)9781424416851
Anomaly detection can detect unknown or new intrusions, and there are increasing interest in it. A normal network behavior model is necessary in any anomaly detection. Assuming that abnormal network behaviors are obviously different from normal ones, there should be some abnormal network packets that are obviously different from normal packets. Normal network packets are disassembled into binary strings whose lengths are l, and all the strings construct a set Unormal. The normal network behavior model is a set Umodel. The length of each element in Umodel is also l. Umodel is made up of those elements each of which matches at least one element in Unormal and doesn't match any other element in U model. Normal network behaviors hide in Umodel. The size of Umodel is discussed. Experimental results indicate that abnormal network behaviors can feasibly be detected with Umodel. Further work is to construct a more feasible Umodel to put into practice.
Radial basis function networks (RBFNs) have shown their capability to be used in classification problems, so that many data mining algorithms have been developed to configure RBFNs. These algorithms need to be given a...
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Radial basis function networks (RBFNs) have shown their capability to be used in classification problems, so that many data mining algorithms have been developed to configure RBFNs. These algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper shows the robustness of a meta-algorithm developed to automatically establish the parameters needed to design RBFNs. Results show that this new method can be effectively used, not only to obtain good models, but also to find a stable set of parameters, available to be used on many different problems.
This paper investigates the behavior of the adaptive dissortative mating genetic algorithm (ADMGA) on dynamic problems and compares it with other genetic algorithms (GA). ADMGA is a non-random mating algorithm that se...
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This paper investigates the behavior of the adaptive dissortative mating genetic algorithm (ADMGA) on dynamic problems and compares it with other genetic algorithms (GA). ADMGA is a non-random mating algorithm that selects parents according to their Hamming distance, via a self-adjustable threshold value. The resulting method, by keeping population diversity during the run, provides new means for GAs to deal with dynamic problems, which demand high diversity in order to track the optima. Tests conducted on combinatorial and trap functions indicate that ADMGA is more robust than traditional GAs and it is capable of outperforming a previously proposed dissortative scheme on a wide range of tests.
A common problem in video surveys in very shallow waters is the presence of strong light fluctuations, due to sun light refraction. Refracted sunlight casts fast moving patterns, which can significantly degrade the qu...
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ISBN:
(纸本)9781424426195
A common problem in video surveys in very shallow waters is the presence of strong light fluctuations, due to sun light refraction. Refracted sunlight casts fast moving patterns, which can significantly degrade the quality of the acquired data. Motivated by the growing need to improve the quality of shallow water imagery, we propose a method to remove sunlight patterns in video sequences. The method exploits the fact that video sequences allow several observations of the same area of the sea floor, over time. It is based on computing the image difference between a given reference frame and the temporal median of a registered set of neighboring images. A key observation is that this difference will have two components with separable spectral content. One is related to the illumination field (lower spatial frequencies) and the other to the registration error (higher frequencies). The illumination field, recovered by lowpass filtering, is used to correct the reference image. In addition to removing the sunflickering patterns, an important advantage of the approach is the ability to preserve the sharpness in corrected image, even in the presence of registration inaccuracies. The effectiveness of the method is illustrated in image sets acquired under strong camera motion containing non-rigid benthic structures. The results testify the good performance and generality of the approach.
Increasing the usability of traditional methods is one of the key issues on future trends in data mining. Nevertheless, most data mining algorithms need to be given a suitable set of parameters for every problem they ...
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Increasing the usability of traditional methods is one of the key issues on future trends in data mining. Nevertheless, most data mining algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper introduces two co-evolutionary algorithms intended to automatically establish the parameters needed to design radial basis function neural networks. Results show that both algorithms can be effectively used to obtain good models, while reducing significantly the number of parameters to be fixed at hand.
Time series composed of a series of data observed according to time sequence. There are large numbers of time series in the field of automatic control. Usually time series data is massive. Sometimes, it is inefficient...
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Time series composed of a series of data observed according to time sequence. There are large numbers of time series in the field of automatic control. Usually time series data is massive. Sometimes, it is inefficient or impractical to mine the original time series data directly. So time series should be represented on higher level. Piecewise Linear Representation is perhaps the most frequently used representation with lower dimensions of index structure and the faster speed of calculation. But PLR needs a parameter k given by users and to make certain k is not easy for the users. We put forward an algorithm of error restricted piecewise linear representation based on special points that can give the number of fitting lines automatically according to a given error e. And it has lower time complexity than other error restricted algorithm, such as sliding windows, Bottom-Up, Top-Down. According to experiments our algorithm leads to less fitting error and better performance than other algorithms. So it would be more efficient in the real application of control fields.
At present, service providers have several incentives to extend the reach of long-lived MPLS paths across domains. Providers, however, will face a number of trade-offs while choosing the optimal set of MPLS paths to b...
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At present, service providers have several incentives to extend the reach of long-lived MPLS paths across domains. Providers, however, will face a number of trade-offs while choosing the optimal set of MPLS paths to be established. In this paper, we focus on the multi-objective decision problem of maximizing the traffic demands to be covered by long-lived MPLS paths from a source domain S to its major destination domains, while minimizing the monetary costs incurred. The problem is formulated subject to a budget constraint, which assures the minimum expected revenue for the provider in S. A major advantage of the analysis and solution proposed in this paper is that it can be easily generalized, and applied in other settings where constrained problems considering maximum coverage vs. cost are critical.
According to complex of gearbox test online, this paper introduces a gearbox test system based on automatic shift manipulator. Optimal shift law plays an important role in improving work efficiency and prolonging serv...
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According to complex of gearbox test online, this paper introduces a gearbox test system based on automatic shift manipulator. Optimal shift law plays an important role in improving work efficiency and prolonging service life of gearbox. It was more intelligent that judging optimal shift by using neural network. The shift procedure was a complicated nonlinear time-varying one, involving many variables, so the expert system was used to control shift procedure. Then it was a more personification procedure owing to fast and accurate operations. Good running state of system proved success for application of neural network and expert system in product test. Moreover the system had some guiding significance in intelligent and personification control of manipulator.
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