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Shao C.,Hefei University of Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Xiao L.,Hefei University of Technology
ICCRD2011 - 2011 3rd International Conference on Computer Research and Development | Year: 2011

NURBS method is extensively used in the field of Geometric Modeling. Since the original NURBS method does not have time factor, it can not display the huge advantage in the dynamics field. In this article, inspired by the segmented nature of basis functions, we propose a time factor method which can be directly applied in reconstruction of missing data, with remarkable performance in matching the original data. This work does not only give an expression of chaotic time series, but also highlights a possible way for the challenge in reconstruction of missing data. © 2011 IEEE. Source


Shao C.,Microsoft | Shao C.,Anhui University of Science and Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Huang C.,Microsoft | Huang C.,Anhui University of Science and Technology
NSWCTC 2010 - The 2nd International Conference on Networks Security, Wireless Communications and Trusted Computing | Year: 2010

Reliability is one of the essential attributes of the dependable software, and an important factor for quantitatively characterizing software quality. Conventional methodology is Software Reliability Growth Model (SRGM), which specifies the form of a random process that describes the behavior of software failures with respect to time. In this paper, we propose that the behavior of software failures possesses determinacy as well as randomness. So we apply chaos theory to software reliability assessment. Based on chaos theory, we can estimate software reliability according to the objective law hidden in the data, unlike the SRGM which usually makes a number of assumptions. This approach avoids the subjectivity in the SRGM. Also, we analyze the actual software failures data sets with the add-weighted one-rank localregion method (AOLM) based on chaos theory and compare the results with the conventional stochastic SRGM JM (Jelinsky-Moranda) model and NHPP (nonhomogeneous poisson process) mode. Comparison results show that the proposed method fits better than the stochastic ones. © 2010 IEEE. Source


Yuan Y.,University of Science and Technology of China | Fanping Z.,University of Science and Technology of China | Fanping Z.,Anhui Province Key Laboratory of Software in Computing and Communication | Guanmiao Z.,University of Science and Technology of China | And 2 more authors.
Communications in Computer and Information Science | Year: 2011

Testing is a critical activity to find software errors. And choosing an effective test suite is the key problem in software testing area. Program invariant, as an attribute of program, can record the implementation state of test case very well and reveal the coverage of program data. In this paper, we integrate adaptive random testing and invariant technology, and present a new method which makes full use of the feedback information of program invariant and invalid case suite to generate the next case. Experiment results show that, compared with other similar methods, running for the same time, our method can achieve higher coverage and faster convergence speed. © 2011 Springer-Verlag. Source


Shao C.,Hefei University of Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Liu Q.,Hefei University of Technology | Wang T.,Hefei University of Technology | And 2 more authors.
Chaos | Year: 2013

Time series is widely exploited to study the innate character of the complex chaotic system. Existing chaotic models are weak in modeling accuracy because of adopting either error minimization strategy or an acceptable error to end the modeling process. Instead, interpolation can be very useful for solving differential equations with a small modeling error, but it is also very difficult to deal with arbitrary-dimensional series. In this paper, geometric theory is considered to reduce the modeling error, and a high-precision framework called Series-NonUniform Rational B-Spline (S-NURBS) model is developed to deal with arbitrary-dimensional series. The capability of the interpolation framework is proved in the validation part. Besides, we verify its reliability by interpolating Musa dataset. The main improvement of the proposed framework is that we are able to reduce the interpolation error by properly adjusting weights series step by step if more information is given. Meanwhile, these experiments also demonstrate that studying the physical system from a geometric perspective is feasible. © 2013 AIP Publishing LLC. Source


He X.,Anhui University of Science and Technology | Shao C.,Anhui University of Science and Technology | Shao C.,Anhui Province Key Laboratory of Software in Computing and Communication | Xiong Y.,Anhui University of Science and Technology
Neurocomputing | Year: 2014

Due to the characteristics of noise and volatility, two similar time series always appear in diverse kinds of distortions, which usually are considered as the combinations of the following basic transformations: noise, amplitude shift, amplitude scaling, temporal scaling, and linear drift. In this paper, a novel similarity measure (SIMshape) invariant to these basic distortions and any combinations of them is proposed. It is parameter-free and easy to implement. Specifically, a multi-scale shape approximation for time series based on Discrete Haar Wavelet Transform, key point extraction and symbolization is presented first; then, based on this proposed representation and a scale-weight factor, a robust similarity measure is proposed. The novelty of SIMshape lies in two aspects as follows: (a) symbolizing key points sequence extracted from approximate wavelet coefficients; (b) adding the scale-weight factor and shape similarity in the similarity criterion. To show the effectiveness and efficiency, SIMshape is compared with other popular methods Euclidean Distance (ED), LB_keogh, Complexity Invariant Distance (CID), and ASEAL (Approximate Shape Exchange ALgorithm) using two indices: the number of kinds of distortions and the degree of distortion. Obtained results show that compared with ED, CID, LB_keogh, and ASEAL, SIMshape has better robustness in synthetic data, and shows better performance in real time series classification. © 2013 Elsevier B.V. Source

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