Long memory of price-volume correlation in metal futures market based on fractal features

An empirical test on long memory between price and trading volume of China metals futures market was given with MF-DCCA method. The empirical results show that long memory feature with a certain period exists in price-volume correlation and a fittther proof was given by analyzing the source of multifractal feature. The empirical results suggest that it is of important practical significance to bring the fractal market theory and other nonlinear theory into the analysis and explanation of the behavior in metal futures market.

New Algorithm for Image Target Recognition Based on Fractal Feature Fusion

By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.

Experimental validation of a signal-based approach for structural earthquake damage detection using fractal dimension of time frequency feature

This article extends a signal-based approach formerly proposed by the authors, which utilizes the fractal dimension of time frequency feature （FDTFF） of displacements, for earthquake damage detection of moment resist frame （MRF）, and validates the approach with shaking table tests. The time frequency feature （TFF） of the relative displacement at measured story is defined as the real part of the coefficients of the analytical wavelet transform. The fractal dimension （FD） is to quantify the TFF within the fundamental frequency band using box counting method. It is verified that the FDTFFs at all stories of the linear MRF are identical with the help of static condensation method and modal superposition principle, while the FDTFFs at the stories with localized nonlinearities due to damage will be different from those at the stories without nonlinearities using the reverse-path methodology. By comparing the FDTFFs of displacements at measured stories in a structure, the damage-induced nonlinearity of the structure under strong ground motion can be detected and localized. Finally shaking table experiments on a 1：8 scale sixteen-story three-bay steel MRF with added frictional dampers, which generate local nonlinearities, are conducted to validate the approach.

Improve Fractal Compression Encoding Speed Using Feature Extraction and Self-organization Network

Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.

Image Splicing Detection Based on Texture Features with Fractal Entropy

Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more severe.As a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked eyes.Thus,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered images.Research on detection of tampered images still carries great challenges.In the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another image.The proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and Kurtosis.The main advantage of FrEp is the ability to extract the texture information contained in the input image.Finally,the“support vector machine”(SVM)classification is used to classify images into either spliced or authentic.Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods.Overall,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.

Exploring the relationship between fractal features and bacterial essential genes

Essential genes are indispensable for the survival of an organism in optimal conditions.Rapid and accurate identifications of new essential genes are of great theoretical and practical significance.Exploring features with predictive power is fundamental for this.Here,we calculate six fractal features from primary gene and protein sequences and then explore their relationship with gene essentiality by statistical analysis and machine learning-based methods.The models are applied to all the currently available identified genes in 27 bacteria from the database of essential genes（DEG）.It is found that the fractal features of essential genes generally differ from those of non-essential genes.The fractal features are used to ascertain the parameters of two machine learning classifiers：Na¨？ve Bayes and Random Forest.The area under the curve（AUC） of both classifiers show that each fractal feature is satisfactorily discriminative between essential genes and non-essential genes individually.And,although significant correlations exist among fractal features,gene essentiality can also be reliably predicted by various combinations of them.Thus,the fractal features analyzed in our study can be used not only to construct a good essentiality classifier alone,but also to be significant contributors for computational tools identifying essential genes.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): II. Illustrative Example

This work highlights the unparalleled efficiency of the “nth-Order Function/ Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-FASAM-N) by considering the well-known Nordheim-Fuchs reactor dynamics/safety model. This model describes a short-time self-limiting power excursion in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted, either intentionally or by accident. This nonlinear paradigm model is sufficiently complex to model realistically self-limiting power excursions for short times yet admits closed-form exact expressions for the time-dependent neutron flux, temperature distribution and energy released during the transient power burst. The nth-FASAM-N methodology is compared to the extant “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-CASAM-N) showing that: (i) the 1st-FASAM-N and the 1st-CASAM-N methodologies are equally efficient for computing the first-order sensitivities;each methodology requires a single large-scale computation for solving the “First-Level Adjoint Sensitivity System” (1st-LASS);(ii) the 2nd-FASAM-N methodology is considerably more efficient than the 2nd-CASAM-N methodology for computing the second-order sensitivities since the number of feature-functions is much smaller than the number of primary parameters;specifically for the Nordheim-Fuchs model, the 2nd-FASAM-N methodology requires 2 large-scale computations to obtain all of the exact expressions of the 28 distinct second-order response sensitivities with respect to the model parameters while the 2nd-CASAM-N methodology requires 7 large-scale computations for obtaining these 28 second-order sensitivities;(iii) the 3rd-FASAM-N methodology is even more efficient than the 3rd-CASAM-N methodology: only 2 large-scale computations are needed to obtain the exact expressions of the 84 distinct third-order response sensitivities with respect to the Nordheim-Fuchs model’s parameters when applying the 3rd-FASAM-N methodology, while the application of the 3rd-CASAM-N methodology requires at least 22 large-scale computations for computing the same 84 distinct third-order sensitivities. Together, the nth-FASAM-N and the nth-CASAM-N methodologies are the most practical methodologies for computing response sensitivities of any order comprehensively and accurately, overcoming the curse of dimensionality in sensitivity analysis.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): I. Mathematical Framework

This work presents the “nth-Order Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (abbreviated as “nth-FASAM-N”), which will be shown to be the most efficient methodology for computing exact expressions of sensitivities, of any order, of model responses with respect to features of model parameters and, subsequently, with respect to the model’s uncertain parameters, boundaries, and internal interfaces. The unparalleled efficiency and accuracy of the nth-FASAM-N methodology stems from the maximal reduction of the number of adjoint computations (which are considered to be “large-scale” computations) for computing high-order sensitivities. When applying the nth-FASAM-N methodology to compute the second- and higher-order sensitivities, the number of large-scale computations is proportional to the number of “model features” as opposed to being proportional to the number of model parameters (which are considerably more than the number of features).When a model has no “feature” functions of parameters, but only comprises primary parameters, the nth-FASAM-N methodology becomes identical to the extant nth CASAM-N (“nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems”) methodology. Both the nth-FASAM-N and the nth-CASAM-N methodologies are formulated in linearly increasing higher-dimensional Hilbert spaces as opposed to exponentially increasing parameter-dimensional spaces thus overcoming the curse of dimensionality in sensitivity analysis of nonlinear systems. Both the nth-FASAM-N and the nth-CASAM-N are incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities of any order with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces.

Assessment of glaucoma using extreme learning machine and fractal feature analysis

Dear Sir,Iam Dr.Kavitha S,from the Department of Electronics and Communication Engineering,Nandha Engineering College,Erode,Tamil Nadu,India.I write to present the detection of glaucoma using extreme learning machine(ELM)and fractal feature analysis.Glaucoma is the second most frequent cause of permanent blindness in industrial

Pulse-to-pulse periodic signal sorting features and feature extraction in radar emitter pulse sequences

A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting.

An improved fast fractal image compression using spatial texture correlation

This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.

Fault Diagnosis Method Based on Fractal Theory and Its Application in Wind Power Systems

The non-linear dynamic theory brought a new method for recognizing and predicting complex non-linear dynamic behaviors. The non-linear behavior of vibration signals can be described by using fractal dimension quantitatively. In this paper, a fractal dimension calculation method for discrete signals in the fractal theory was applied to extract the fractal dimension feature vectors and classified various fault types. Based on the wavelet packet transform, the energy feature vectors were extracted after the vibration signal was decomposed and reconstructed. Then, a wavelet neural network was used to recognize the mechanical faults. Finally, the fault diagnosis for a wind power system was taken as an example to show the method's feasibility.

Fractal Characteristic of Soil in Typical Debris Flow-Triggering Region:A Case Study in Jiangjia Ravine of Dongchuan, Yunnan

The structural features of soil in debris flow-triggering region play an important role in the formation and evolution of debris flow. In this paper, a case study on the fractal of soil particle-size distribution （PSDFs） and pore-solid （PSFs） in Jiangjia Ravine was conducted. The results revealed that the soil in Jiangjia Ravine had significant fractal features and its PSDF and PSF had the same variation trend despite different type of soils in debris flow-triggering region： residual soil （RS） 〉 debris flow deposit （DFD）~clinosol （CL）, their fractal dimension of PSDFs are respectively between 2.62 and 2.96, 2.52 and 2.68, 2.37 and 2.52; and the fractal dimension of PSFs are respectively between 2. 75 and 2.95, 2. 57 and 2. 72, 2.59 and 2.64. The fractal dimension of soil reflected its complexity as a self-organizing system and was closely related to the evolution of soil in debris flow- triggering region.

An image retrieval system based on fractal dimension

This paper presents a new kind of image retrieval system which obtains the feature vectors of images by estimating their fractal dimension; and at the same time establishes a tree structure image database. After preprocessing and feature extracting, a given image is matched with the standard images in the image database using a hierarchical method of image indexing.

FACE RECOGNITION BASED ON WAVELET-CURVELET-FRACTAL TECHNIQUE

In this paper,a novel face recognition method,named as wavelet-curvelet-fractal technique,is proposed. Based on the similarities embedded in the images,we propose to utilize the wave-let-curvelet-fractal technique to extract facial features. Thus we have the wavelet’s details in diagonal,vertical,and horizontal directions,and the eight curvelet details at different angles. Then we adopt the Euclidean minimum distance classifier to recognize different faces. Extensive comparison tests on dif-ferent data sets are carried out,and higher recognition rate is obtained by the proposed technique.

Texture Filters and Fractal Dimension on Image Segmentation

Texture analysis is important in several image segmentation and classification problems. Different image textures manifest themselves by dissimilarity in both the property values and the spatial interrelationships of their component texture primitives. We use this fact in a texture discrimination system. This paper focuses on how to apply texture operators based on co-occurrence matrix, texture filters and fractal dimension to the problem of object recognition and image segmentation.

Description of desertification evolution in Fuxin district of Liaoning province based on fractal theory

Desertification is the most serious environmental problem in the world today. Fractal feature of granularity composition was studied by using the fractal theory in view of desertification soil in Fuxin district, thereby evolution patterns of desertification was promulgated. The result shows that the self-formation degree of the developing desertification areas is higher than the relatively steady desertification areas. Evolution of desertification is beginning of forming sandy soil of framework composition, and then the sandy soil be came complex by the effect of environment, climate and anthropo-activity.

分形界面吸附行为初探

煤层气吸附解吸机理研究是揭示煤层气成藏机理及高效开发煤层气的基础。已有研究表明,煤储层孔隙结构非常复杂且具有分形特征,煤层气在孔-固界面的吸附行为明显受到煤孔隙结构特征的影响。对比分析几种常用气-固界面上的吸附模型,总结这些模型的特点和适用条件,指出当前吸附模型在分形界面吸附行为的描述和应用上均未摆脱吸附选择性的平稳假设,尚未考虑吸附厚度的尺度不变特征。而分形拓扑理论可以有效标定分形对象的尺度不变属性,为分形界面的等效表征提供了理论支撑。因此,结合上述模型对气-固界面吸附行为的描述,借助分形拓扑理论提出煤储层孔-固界面上分形吸附行为的相关假设及其控制机理,构建基于吸附拓扑的单层吸附模型。在此基础上,通过设置不同的吸附拓扑参数组合获取其等温吸附曲线,分析得出,随着吸附压力的升高,吸附覆盖率表现出指数、线性和对数3种不同的增长趋势,而吸附热表现为对数减小趋势。结果显示,不同的吸附拓扑参数组合会得到不同类型的等温吸附曲线,在一定程度上弥补了Langmuir方程只能描述单一类型等温吸附线的不足。为了验证吸附模型的适用性,结合沁水盆地武乡区块富有机质泥页岩的液氮吸附实验数据,对比了实际等温吸附曲线与模拟吸附曲线的差异。结果表明,通过调整各吸附拓扑参数之间的组合关系,可以使等温吸附模拟曲线与实际吸附曲线的趋势始终保持一致。最后,探讨了吸附解吸机理的研究方向,类比电子层提出了吸附层的概念,指出发展分形动力学描述模型是解释煤层气吸附解吸规律的关键。

云南永胜古滑坡堰塞湖沉积物粒度多重分形特征及其指标适用研究

本文以云南永胜县下院滑坡堰塞湖沉积物为研究对象,依据粒度测试结果采用传统粒度指标与分形理论相结合的方式对其沉积环境进行系统化研究,探讨各传统指标在堰塞湖沉积解析中的适用性,探索多重分形理论在沉积学中的应用价值。研究结果表明:①堰塞湖沉积物粒度统计参数明显不同于其他环境下的沉积物,具有河湖相沉积独特的分布曲线形式、分选状态和分形特征。优势粒级在分形计算中起主导地位,全局分形维数与分选系数有着良好的对应关系。不同的分形维数对应着不同的堰塞湖沉积条件,分形维数在堰塞湖粒度解析中有着很好的运用前景;②多重分形结果显示2种不同时期的堰塞湖沉积物均以高聚集度组分为主体,表明粒径分布集中,反映出堰塞湖沉积过程中水动力的稳定性,沉积来源的唯一性,进一步证明了该堰塞湖曾长期存在。细粒组沉积粒径分布范围较窄,分布相对集中,优势粒组多但单个含量低,尾端含量低,内部分异较大;粗粒组分布呈局部集中整体分散的趋势,高聚集度粒组数量少但含量高,低聚集度粒组数量多但单个含量低,呈现中间高,四周低的特点。③多重分形分析表明Δα、Δf在沉积物粒度分析中能够解析粒组内部分布特征,具有传统指标不可替代的作用,D_(0)和D_(1)两者结合可作为沉积环境解析的替代指标,与其他q阶多重分形联合可进一步作为堰塞湖解析指标,而D_(1)/D_(0)和D2则存在一定局限。

高阶煤吸附孔结构特征及其对甲烷吸附能力的影响

孔隙结构对煤层吸附甲烷的能力有显著影响,但目前对高阶煤吸附孔结构特征及其对甲烷吸附能力的影响研究较少。以贵州兴安煤业有限公司糯东煤矿高阶煤样为研究对象,采用低温N2吸附和低温CO_(2)吸附试验,结合分形理论研究了高阶煤吸附孔的孔隙结构特征,并通过高压等温甲烷吸附试验,分析了煤储层物性、孔隙结构特征和分形维数对甲烷吸附能力的影响。结果表明:①高阶煤储层孔隙形态较为单一,多数为两端开放的平行板孔和狭缝型孔,微孔在煤的孔隙结构中占主导地位,其孔体积和孔比表面积占比均大于98%,为气体的富集提供了空间。②以不同孔径段的孔体积占比为权重计算高阶煤孔隙的综合分形维数,微孔分形维数在综合分形维数中占主导地位;煤样孔隙结构具有明显的分形特征,孔隙非均质性较强。③Langmuir模型能很好地描述高阶煤的吸附行为,煤储层物性、孔隙结构和分形维数对甲烷吸附能力影响显著,Langmuir体积与最大镜质体反射率、镜质组含量、灰分含量和水分含量呈线性正相关关系,与惰质组含量呈线性负相关关系;Langmuir体积与吸附孔的孔比表面积和孔体积均呈线性正相关关系,Langmuir体积与分形维数呈弱线性关系。研究结果可为黔西南地区高阶煤层气勘探开发及煤矿瓦斯灾害防治提供理论指导。