摘要：
Multifractal theory has been widely used in different kinds of fields. In this paper, methods were proposed to extract two kinds of multifractal descriptors of gray series and two-dimensional surfaces for gray image based on the multifractal detrended fluctuation analysis. The proposed multifractal parameters can be well described by texture feature through the test of some textures. Three aspects of experiments have been conducted to verify the robustness of the proposed parameters, which include noise immunity, degree of image blurring and compression ratio. Comparisons were conducted between the proposed parameters and other kinds of texture feature parameters calculated by the standard multifractal analysis, the method of differential box counting and the methods of gray level co-occurrence matrix. Results demonstrate that the proposed exponents of H(2) and h(2) have great noise immunity and are robust to image compression and blurring.
摘要：
In recent years, the popular multifractal detrended fluctuation analysis (MF-DFA) is extended to two-dimensional (2D) version, which has been applied in some field of image processing. In this paper, based on the 2D MF-DFA, a novel multifractal estimation method for images, which we called the local multifractal detrended fluctuation analysis (LMF-DFA), is proposed to recognize and distinguish 20 types of tea breeds. A set of new multifractal descriptors, namely the local multifractal fluctuation exponents is defined to portray the local scaling properties of a surface. After collecting 10 tea leaves for each breed and photographing them to standard images, the LMF-DFA method is used to extract characteristic parameters for the images. Our analysis finds that there are significant differences among the different tea breeds' characteristic parameters by analysis of variance. Both the proposed LMF-DFA exponents and another classic parameter, namely the exponent based on capacity measure method have been used as features to distinguish the 20 tea breeds. The comparison results illustrate that the LMF-DFA estimation can differentiate the tea breeds more effectively and provide more satisfactory accuracy.
关键词：
Drug R&D;L-system;fractals;medical plants;quasi binary-trees;toxicity
摘要：
Background: Searching the drug molecules from the medicinal plants become more and more popular given that herbalcomponents have been widely considered to be safe.In medical virtual plant studies, development rules are difficult to be extracted, the construction of plant organs is highlydependent on equipment and the process is complicated.Aim: To establish three-dimensional structural virtual plant growth model.Methods: The quasi-binary tree structure and its properties were obtained through the research of theory on binary tree,then the relationship between quasi-binary tree structure and plant three-dimensional branching structure model was analyzed,and the three-dimensional morphology of plants was described.Results: A three-dimensional plant branch structure pattern extracting algorithm based on quasi-binary tree structure. Byusing 3-D L-system method, the extracted rules were systematized, and standardized. Further more, we built a comprehensiveL-model system. With the aid of graphics and PlantVR, we implemented the plant shape and 3-D structure’s reconstruction.Conclusion: Three-dimensional structure virtual plant growth model based on time- controlled L-system has been successfullyestablished.Keywords: Drug R&D, toxicity, medical plants, fractals; L-system; quasi binary-trees.
关键词：
Optical color image encryption;Phase truncation operation;The position multiplexing
摘要：
We propose an optical color image cryptosystem based on position multiplexing technique and phase truncation operation. Compared with the reported color image encryption method, we employ the position multiplexing technique to encrypt the color image in only one spatial channel. Meanwhile, our proposed method can maintain the nonlinear characteristic of the cryptosystem and avoid various types of the currently existing attacks, especially the iterative attack. Simulation results are presented to demonstrate the security and robustness performance of the proposed method.
关键词：
Auto-correlation;Power time series;Multifractal detrended analysis;Multifractal detrended cross-correlation analysis;Time-delay
摘要：
Multifractal theory has been widely used in kinds of field. In this paper, methods were proposed to study in the power-law auto-correlation and cross-correlation of power operating data based on multifractal detrended analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DXA). We find that both the price and the load time series in California power market and PJM power market exhibit long-term correlation. And the cross-correlation behaviors of the two series in each power market and between the two markets are also analyzed by the method MF-DXA after testing the existence of the cross-correlation of the above power operating data. However, there are some differences in the cross-correlation behaviors between the two markets. It shows that the cross-correlation of the price and the load is significant in every time periods in California 1999 power market, but in the year of 2000 in the same region market, the cross-correlation is insignificant in most time periods. Meanwhile, we conclude that cross-correlation is weaker in the California market than in the PJM market by studying the two consecutive years of the California 1999-2000 and PJM 2001-2002 power markets. We also discuss how the time intervals affect the cross-correlation exponents of the power operating data based on time-delay MF-DXA. An interesting finding is that the biggest cross-correlation exponent of the two series appeared in about 12 days time delay for the PJM 2001 power market and strongest cross-correlation in the California 1999 power market is found in lots of cyclical time intervals.
摘要：
A novel method, which we called the analogous multifractal cross-correlation analysis, is proposed in this paper to study the multifractal behavior in the power-law cross-correlation between price and load in California electricity market. In addition, a statistic rho(AMF-XA), which we call the analogous multifractal cross-correlation coefficient, is defined to test whether the cross-correlation between two given signals is genuine or not. Our analysis finds that both the price and load time series in California electricity market express multifractal nature. While, as indicated by the rho(AMF-XA) statistical test, there is a huge difference in the cross-correlation behavior between the years 1999 and 2000 in California electricity markets. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4793355]
期刊：
BMC Systems Biology,2013年7(4):1-15 ISSN：1752-0509
通讯作者：
Wang, Jianxin
作者机构：
[Ren, Jun; Li, Min; Wang, Jianxin] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.;[Ren, Jun] Hunan Agr Univ, Coll Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.;[Wang, Lusheng] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China.
通讯机构：
[Wang, Jianxin] C;Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.
关键词：
Protein Complex;Neighbor Vertex;Dense Subgraph;Undirected Weighted Graph;Predict Protein Complex
摘要：
Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity. To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, f
ρ
, as f
ρ
= (density)
ρ
*(modularity)1-ρ, and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<ρ<1). Compared with the results of seven competing protein complex detection methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC) in S.cerevisiae and E.coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity. By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.
关键词：
Electrochemical performances;LiCoO2;Lithium ion batteries;Percolation theory;Polyaniline
摘要：
Polyaniline synthesized by chemical polymerization is replace acetylene black to be used as the conductive additive of LiCoO2 cathode. FTIR shows that polyaniline prepared possesses the typical features of conductive emeraldine salts. The conductivity of polyaniline is 15.29 S cm-1 which is more than that of acetylene black. Polyaniline has some discharge capacity in the potential range of cathode, so it can be used as the cathode material. The conductivity changes of LiCoO2 cathode film with the increase of the content of conductive additive conform to the percolation theory. The conductivity of cathode reaches its maximum value of 4.02 × 10-1 S cm-1 when the content of polyaniline reaches 15 wt. %, which is much bigger than that of acetylene black. The electrochemical performances of LiCoO2 cathode is improved remarkably. The discharge capacity of LiCoO2 cathode is 95.9 mAh g-1 at the current density of 170 mA g-1. Its charge transfer resistance is much lower than that of acetylene black in the 20 cycle. The resilience of polyaniline reduces the expansion and contraction of LiCoO2, which keeps the conductive network integrity.
摘要：
Massive events can be produced today because of the rapid development of the Internet of Things (IoT). Complex event processing, which can be used to extract high-level patterns from raw data, has become an essential part of the IoT middleware. Prediction analytics is an important technology in supporting proactive complex event processing. In this paper, we propose the use of dynamic Bayesian model averaging to develop a high-accuracy prediction analytic method for large-scale IoT application. This method, which is based on a new multilayered adaptive dynamic Bayesian network model, uses Gaussian mixture models and expectation-maximization inference for basic Bayesian prediction. Bayesian model averaging is implemented by using Markov chain Monte Carlo approximation, and a novel dynamic Bayesian model averaging method is proposed based on event context clustering. Simulation experiments show that the proposed prediction analytic method has better accuracy compared to traditional methods. Moreover, the proposed method exhibits acceptable performance when implemented in large-scale IoT applications.
