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Multi-objective uncertainty-wise test case minimization focuses on selecting a minimum number of test cases to execute out of all available ones while maximizing effectiveness (e.g., coverage), minimizing cost (e.g., time to execute test cases), and at the same time optimizing uncertainty-related objectives. In our previous unpublished work, we developed four uncertainty-wise test case minimization...
RFID plays a very important role as auto-identification solution for many industrial applications. In electric power industry, UHF RFID tags are attached on smart meters, and a tag provides a unique electronic identification for a smart meter, so RFID is applied to make asset inventory for smart meters in a warehouse. However, there are just about 95% of tags that are readable for their reader because...
In the biomedicine domain, a large number of papers are published every day, which is crucial to search for the relevant answers for the user's query. However, documents are not exactly what the users want. Instead, snippets, small segments from the documents, are more proper to meet the requirement of the users. Hence, this paper proposes a biomedical snippet retrieval framework to exactly locate...
Principle of support vector regression method is investigated. Genetic algorithm is adopted to search the optimal SVR parameters to improve the generalization performance of SVR. Then an improved SVR method based on intelligent computing is put forward. At last, the proposed method is used in the prediction of ecological tourism economy. Different kernel functions are used for training data, and the...
For the issues that the cropped level of image is detected and the cropped region of image is determined, a new scheme is proposed that is based on SIFT (Scale-invariant feature transform) for the regular clipping operation under the situation of existing original image. Features points of original and testing images are extracted by SIFT algorithm, and the matching are done between the corresponding...
In this paper, a new abnormal activity detection algorithm is proposed for multi-camera surveillance applications. The proposed algorithm models the entire scene covered by the multi-camera system as a network. In this network, each node corresponds to a segmentation of the entire scene and each edge represents the activity correlation between the corresponding segmentations. Based on this network,...
In this paper, we propose a methodology of the automated bitstream generation for conducting high-testability FPGA tests. In order to study the efficiency of our solution we will explore our methodology in the test of an SOI-based FPGA. We use a semi-automated approach of the bitstream generation for ease of test vector design with high functionality and fault coverage. The methodology from this research...
This paper introduces the framework and the design, it mainly analyses the design methods of hardware and software in the main circuit, the system has characteristics of highly auto-controlling, avoiding error, using conveniently, and good military and business benefits.
Choquet integral with regards to a non-additive set function μ is a useful combination tool when we consider the interactions between classifiers. This combination method works very well at the expense of run time and the memory space. This paper introduces samples reduction technology to degrade the complexity of determining the non-additive set functions μ which is determined by genetic algorithm...
Aiming at the problem of ZigBee tree routing algorithm in which the solution may not be optimal and some nodes may exhaust the energy as a result of heavy transmissions, an optimized ZigBee tree routing algorithm based on the energy-balance. The optimized algorithm imports neighbor-table and the depth of nodes to make sure the routing local optimal in routing hops. This paper also considers the residual...
In this paper, a new prediction model, based on chaos theory and BP artificial neural network, is developed to predict the risk of credit card transactions. Embedding dimension of phase-space reconstruction is used to determine network structure, and overcomes the dependence on large amount of samples. Experiments shows that the method based on combination of chaos theory and neural network can improve...
The self wide-range (26%~76%), fine-scale (34 ps) duty cycle adjustment technique with high-precision (28 ps) calibration circuit are proposed for at-speed delay test and performance binning. Test chip DFT strategies are validated fully function work by instruments and HOY wireless test system.
Stationarity is often found in session-to-session transfers of brain computer interfaces (BCIs). To cope with the problem, a framework based on common spatial patterns (CSP), linear discriminant analysis (LDA), and covariate shift adaptation methods is proposed. Covariate shift adaptation is an effective method which can adapt to the testing sessions without the need for labeling the testing session...
The purpose of this paper is to examine the empirical relationship between tourism foreign exchange income and economic growth based on Chinese province-level data. We build a sample which comprises annual observations in 30 Chinese provinces over the period 1995 to 2007. With this sample the number of time series observation is relatively large and of the same order of magnitude as the number of...
A new dynamic selective neural network ensemble method for fault diagnosis of steam turbine is proposed. Firstly, a great number of diverse BP neural network models are produced. Secondly, the error matrix is calculated and the K-nearest neighbor algorithm is used to predict the generalization errors of different neural networks on each testing sample. Thirdly, the individual networks whose generalization...
In this paper, we present a new SVM model to calculate the optimal value of cost parameter C for particular problems of linearity non-separability of data. A lower bound, positive number, C0 is required to provide for avoiding choosing a candidate set of C. Numerical experiments show that this model for choice of is suitable for solving SVM problems.
Surface reconstruction based on Support Vector Machine (SVM) is a hot topic in the field of 3-dimension surface construction. But it is difficult to apply this method to cloud points. A reconstruction method based on segmented data is proposed to accelerate SVM regression process from cloud data. First, by partitioning the original sampling data set, several training data subsets and testing data...
Surface reconstruction based on Support Vector Machine (SVM) is a hot topic in the field of 3D surface construction. SVM based method for surface reconstruction can reduce the noise in the sampled data as well as repair the holes. However, the regress speed of SVM is too slow to reconstruct surface quickly from cloud points data set which has a lot of points. In this paper, a feature-preserved nonuniform...
Image segmentation is one of the most critical tasks in image processing. Entropy-based threshold value is one of the most efficient techniques for image segmentation. The non-extensive (or non-additive) entropy is a recent development in statistical mechanics. In this paper, a two-dimensional Tsallis entropy (TE) with non-additive information content based on co-occurrence matrix constructed by the...
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