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Journal articles on the topic 'Fractal Detection'
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1
Zhang, Pei-Lin, Bing Li, Shuang-Shan Mi, Ying-Tang Zhang, and Dong-Sheng Liu. "Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers." Shock and Vibration 19, no. 6 (2012): 1373–83. http://dx.doi.org/10.1155/2012/438789.
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Vibration signals acquired from bearing have been found to demonstrate complicated nonlinear characteristics in literature. Fractal geometry theory has provided effective tools such as fractal dimension for characterizing the vibration signals in bearing faults detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant fractal dimension at all scales may not be true. Motivated by this fact, this work explores the application of the multi-scale fractal dimensions (MFDs) based on morphological cover (MC) technique for bearing fault diagnosis. Vibration signals from bearing with seven different states under four operations conditions are collected to validate the presented MFDs based on MC technique. Experimental results reveal that the vibration signals acquired from bearing are not critical self-similar fractals. The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. Experimental results demonstrate the MFDs to be a desirable approach to improve the performance of bearing fault diagnosis.
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Potapov, Alexander A. "Fractal applications in radio electronics as fractal engineering." Radioelectronics. Nanosystems. Information Technologies. 14, no. 3 (2022): 215–32. http://dx.doi.org/10.17725/rensit.2022.14.215.
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The use of the fractal paradigm is presented - the main directions for introducing textures, fractals, fractional operators, dynamic chaos and methods of nonlinear dynamics for the design and creation of real technical projects in radio electronics - fractal radio systems, taking into account the hereditarity, non-Gaussianity and scaling of physical signals and fields. The substantiation of the use of fractal-scaling and texture methods for the synthesis of fundamentally new topological texture-fractal methods for detecting signals in the space-time channel of scattering waves (a new type of radar) is discussed. It is shown that the use of fractal systems, sensors and nodes is a fundamentally new solution that significantly changes the principles of constructing intelligent radio engineering systems and devices. It is shown that the use of computational dielectric metasurfaces brings to a new level all the functional characteristics of a multifunctional system of topological texture-fractal processing of signals and fields in solving classical problems of detection, measurement, recognition and classification by intelligent radio engineering systems and devices. The concept of "fractal engineering" is introduced, the methodology of its use is discussed.
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Polattimur, Rukiye, Mehmet Süleyman Yıldırım, and Emre Dandıl. "Fractal-Based Architectures with Skip Connections and Attention Mechanism for Improved Segmentation of MS Lesions in Cervical Spinal Cord." Diagnostics 15, no. 8 (2025): 1041. https://doi.org/10.3390/diagnostics15081041.
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Background/Objectives: Multiple sclerosis (MS) is an autoimmune disease that damages the myelin sheath of the central nervous system, which includes the brain and spinal cord. Although MS lesions in the brain are more frequently investigated, MS lesions in the cervical spinal cord (CSC) can be much more specific for the diagnosis of the disease. Furthermore, as lesion burden in the CSC is directly related to disease progression, the presence of lesions in the CSC may help to differentiate MS from other neurological diseases. Methods: In this study, two novel deep learning models based on fractal architectures are proposed for the automatic detection and segmentation of MS lesions in the CSC by improving the convolutional and connection structures used in the layers of the U-Net architecture. In our previous study, we introduced the FractalSpiNet architecture by incorporating fractal convolutional block structures into the U-Net framework to develop a deeper network for segmenting MS lesions in the CPC. In this study, to improve the detection of smaller structures and finer details in the images, an attention mechanism is integrated into the FractalSpiNet architecture, resulting in the Att-FractalSpiNet model. In addition, in the second hybrid model, a fractal convolutional block is incorporated into the skip connection structure of the U-Net architecture, resulting in the development of the Con-FractalU-Net model. Results: Experimental studies were conducted using U-Net, FractalSpiNet, Con-FractalU-Net, and Att-FractalSpiNet architectures to detect the CSC region and the MS lesions within its boundaries. In segmenting the CSC region, the proposed Con-FractalU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) score of 98.89%. Similarly, in detecting MS lesions within the CSC region, the Con-FractalU-Net model again achieved the best performance with a DSC score of 91.48%. Conclusions: For segmentation of the CSC region and detection of MS lesions, the proposed fractal-based Con-FractalU-Net and Att-FractalSpiNet architectures achieved higher scores than the baseline U-Net architecture, particularly in segmenting small and complex structures.
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Sheng, Li Lian. "Fractal-Based Outlier Detection Algorithm over RFID Data Streams." International Journal of Online Engineering (iJOE) 12, no. 1 (2016): 35. http://dx.doi.org/10.3991/ijoe.v12i1.5171.
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Nowadays, Radio frequency identification (RFID) has been extensively deployed to retailing, supply chain management, object recognition, object monitoring and tracking and many other fields. Detecting outliers in RFID data streams can help us find abnormal activities and thus avoid disasters. In order to detect outliers in RFID data streams efficiently and effectively, we proposed a fractal based outlier detection algorithm. Firstly, we built a monotone searching space based on the self-similarity of fractal. Then, we proposed two piecewise fractal models for RFID data streams, and presented an outlier detection algorithm based on the piecewise fractal model. Finally, we validated the efficiency and effectiveness of the proposed algorithm by massive experiments.
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P.Tharaniya. "Border Detection of Skin Cancer Cells over Fractal Dimension Analysis and Image Processing Techniques." Communications on Applied Nonlinear Analysis 32, no. 3s (2024): 45–59. https://doi.org/10.52783/cana.v32.2533.
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Fractal dimension analysis is a novel technique that uses the self-similarity qualities of fractals to identify irregular forms, such as those prevalent in diseased tissues, in order to detect the borders of skin cancer cells. Acquire detailed pictures of skin tissue samples that have cancer cells in them. A variety of imaging methods, including microscopy and medical imaging tools like MRIs and CT scans, can be used for this. Determine the image's fractal dimension by applying suitable methods, like the fractal signature method or box-counting. A geometric shape's complexity is measured by its fractal dimension, and because malignant cells have uneven edges, they typically show higher complexity. To increase the border recognition process' accuracy, clean the photos to get rid of noise and boost contrast. Here, methods such as morphological procedures, histogram equalization, and median filtering can be used. To increase the border detection system's accuracy and resilience, fine-tune the parameters and algorithms in light of the validation results. A reliable approach for identifying the borders of skin cancer cells can be created by fusing fractal dimension analysis with image processing methods. This will help with early detection and therapy planning. Based on the fractal dimension, choose an appropriate threshold value to divide the image into zones of interest. This stage aids in the malignant cells' separation from the surrounding tissue.
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UTHAYAKUMAR, R., and G. JAYALALITHA. "BORDER DETECTION OF SKIN CANCER CELLS WITH FRACTAL DIMENSION." Fractals 17, no. 02 (2009): 171–80. http://dx.doi.org/10.1142/s0218348x09004417.
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In this paper we study a model of skin cancer (MM) in vitro, using geometry of fractals as the method of analysis. The fractal dimensions of moles (skin cancer cells) growth pattern have been measured by using the methods of Box-counting method (DB) and Sausage method (DS). The cell growth of this cancer can be modeled by Hidden Markov model (HMM) and percolation model which are depending upon the time complexity. From these models we can find the shape of the irregularity border by using the probability distribution of the cells. The variation in the irregular border of the skin cancer has been found out using ANOVA test and cell's compactness. The fractal approach led to very promising results which improved the determination and examination of the stage of skin cancer.
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MCGINLEY, PATTON, ROBIN G. SMITH, and JEROME C. LANDRY. "FRACTAL DIMENSIONS OF MYCOSIS FUNGOIDES." Fractals 02, no. 04 (1994): 493–501. http://dx.doi.org/10.1142/s0218348x94000715.
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Prior to an investigation of early diagnosis of mycosis fungoides (MF) using fractal geometry, we set out to see if MF lesions are fractal in nature. We analyzed three aspects of MF lesions: the dermoepidermal profile of photomicrographs of patch stage lesions and normal skin, the perimeter of patch and plaque stage lesions, and the size distribution of patch and plaque lesions on the skin surface. The perimeter of plaque lesions was measured on close-up photographs by the divider walk method using various step sizes. Based on the perimeter values, the fractal dimension was determined. The dermoepidermal profile of MF patch lesions was analyzed by the divider walk method for self-affine fractals. The size distribution of MF patch and plaque lesions was determined by counting the number of patch and plaque lesions with an area greater than or equal to a specific size A on scaled photographs of a 19.6 cm × 19.6 cm affected region. A plot of number of lesions with area greater than or equal to A vs. lesion area on log-log paper allows the detection of a power-law distribution, indicative of one type of self-similar fractals. The dermoepidermal profile of patch stage lesions and normal skin was found to be self-affine fractals. Global measurements of normal thin skin and of patch stage lesions were distinct. All observed patch and plaque lesion area distributions were a fractal set. The perimeter of non-confluent plaque lesions was not fractal. This work revealed fractal dimensions in two aspects of MF lesions. Further investigation of application of fractal geometry to the diagnosis and staging of MF is planned.
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Li, Chang Hai, Yong Sheng Liang, Kong Qiang Zhu, and Yan Chun Liu. "GPR Target Detection Based on Fractal Dimension." Applied Mechanics and Materials 321-324 (June 2013): 785–89. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.785.
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To achieve rapid target detection by GPR (Ground Penetrating Radar),the article introduces fractal dimension as the characteristic to describe the complexity of the one-dimensional discrete signal and puts forward an algorithm for rapid target detection by GPR. First, calculate the fractal dimension of signals at different points in the direction of the survey line and depth direction after wavelet transform processing to obtain the curve of fractal dimension on two directions. Finally, determine the suspicious region in the curve of fractal dimension based on the judgment criterion to fulfill target detection. The measured data testify that the method can achieve the rapid detection on a specific target in a certain context.
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HU, JING, JIANBO GAO, FRED L. POSNER, YI ZHENG, and WEN-WEN TUNG. "TARGET DETECTION WITHIN SEA CLUTTER: A COMPARATIVE STUDY BY FRACTAL SCALING ANALYSES." Fractals 14, no. 03 (2006): 187–204. http://dx.doi.org/10.1142/s0218348x06003210.
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Sea clutter refers to the radar returns from a patch of ocean surface. Accurate modeling of sea clutter and robust detection of low observable targets within sea clutter are important problems in remote sensing and radar signal processing applications. Due to lack of fundamental understanding of the nature of sea clutter, however, no simple and effective methods for detecting targets within sea clutter have been proposed. To help solve this important problem, we apply three types of fractal scaling analyses, fluctuation analysis (FA), detrended fluctuation analysis (DFA), and the wavelet-based fractal scaling analysis to study sea clutter. Our analyses show that sea clutter data exhibit fractal behaviors in the time scale range of about 0.01 seconds to a few seconds. The physical significance of these time scales is discussed. We emphasize that time scales characterizing fractal scaling break are among the most important features for detecting patterns using fractal theory. By systematically studying 392 sea clutter time series measured under various sea and weather conditions, we find very effective methods for detecting targets within sea clutter. Based on the data available to us, the accuracy of these methods is close to 100%.
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Kirichenko, Lyudmyla, Yulia Koval, Sergiy Yakovlev, and Dmytro Chumachenko. "Anomaly Detection in Fractal Time Series with LSTM Autoencoders." Mathematics 12, no. 19 (2024): 3079. http://dx.doi.org/10.3390/math12193079.
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This study explores the application of neural networks for anomaly detection in time series data exhibiting fractal properties, with a particular focus on changes in the Hurst exponent. The objective is to investigate whether changes in fractal properties can be identified by transitioning from the analysis of the original time series to the analysis of the sequence of Hurst exponent estimates. To this end, we employ an LSTM autoencoder neural network, demonstrating its effectiveness in detecting anomalies within synthetic fractal time series and real EEG signals by identifying deviations in the sequence of estimates. Whittle’s method was utilized for the precise estimation of the Hurst exponent, thereby enhancing the model’s ability to differentiate between normal and anomalous data. The findings underscore the potential of machine learning techniques for robust anomaly detection in complex datasets.
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Xiong, Gang, Shu-Ning Zhang, and Hui-Chang Zhao. "The Impact Mechanism of Fractal Noise on PN Code Detection System." Fluctuation and Noise Letters 13, no. 02 (2014): 1450017. http://dx.doi.org/10.1142/s0219477514500175.
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Taking the pseudo-random phase modulated CW radar for example, this paper studies the impact mechanism of a class of non-stationary fractal noise on PN code detection system, especially signal mixing and matching filter. The cross correlation function, power spectrum function and average power of pseudo-random signal and fractal noise are deduced, compared with the impact of white noise on the pseudo code detection system. We analyze the impact mechanism of three kinds of sea clutter model, namely fractal Brownian model (FBM), the multifractal (MF) model and the non-stationary random fractal model (e.g., infinitely divisible cascades, IDC), on the pseudo-random code detection system, and demonstrate the reason why the multi-scale filtering method in wavelet domain and the MF methods fail to eliminate the effect of sea clutter. Based on the natural sea clutter data, we simulate and analyze the influence of white noise and fractal noise comparatively on detection system, which indicates that the effect of fractal noise cannot be inhibited effectively by the traditional correlation detection and MF analysis, and finally we put forward possible solutions.
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Sheluhin, O., S. Rybakov, and A. Vanyushina. "Modified Algorithm for Detecting Network Attacks Using the Fractal Dimension Jump Estimation Method in Online Mode." Proceedings of Telecommunication Universities 8, no. 3 (2022): 117–26. http://dx.doi.org/10.31854/1813-324x-2022-8-3-117-126.
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The paper considers a modification of the well-known algorithm for detecting anomalies in network traffic using a real-time fractal dimension jump estimation method. The modification uses real-time thresholding to provide additional filtering of the estimated fractal network traffic dimension. The accuracy of the current estimate of the fractal dimension and the reliability of anomaly detection in network traffic in online mode is improved by adding extra filtering to the algorithm.
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Demers, Matthew. "Edge detection using fractal imaging." International Journal of Mathematical Modelling and Numerical Optimisation 3, no. 4 (2012): 266. http://dx.doi.org/10.1504/ijmmno.2012.049602.
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Blagoveshchenskii, Yu N. "Statistical Problems of Fractal Detection." Journal of Mathematical Sciences 119, no. 3 (2004): 361–68. http://dx.doi.org/10.1023/b:joth.0000009367.90913.37.
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Xie, Xu Liang, and Ali Hui. "Edge Detection of IR Image via Chirplet Fractal Dimension." Applied Mechanics and Materials 58-60 (June 2011): 1877–81. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1877.
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A new idea, using chirplet as the staff to define fractal dimension, is proposed in this paper, based on self- similitude of knowing essence of things from collectivity to part, from macroscopy to microcosm, in fractal theory and chirplet transformation. Chirplet fractal dimension is defined as the sum of high-frequency values of decomposed signals. The edge of infrared image is detected through chirplet fractal dimension, experimental results show that this new algorithm is simple and effective to detect whole contour and detail information, and is better than other traditional operators.
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He, Deyi, and Chusheng Liu. "An Online Detection Method for Coal Dry Screening Based on Image Processing and Fractal Analysis." Applied Sciences 12, no. 13 (2022): 6463. http://dx.doi.org/10.3390/app12136463.
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In coal dry screening, online detection for screening efficiency is a significant challenge. Notwithstanding, the method of image processing is strenuous to implement in this field due to the complex surface texture of shattered coal. This method identifies the fractal phenomenon before and after coal screening is discovered for the indirect detection of screening efficiency. For better fractal dimension distribution, an image denoising and filter method for wiping off the coal image surface texture is applied. Additionally, an enhanced Kirsch edge-detection algorithm is employed to obtain coal particle edges. Furthermore, the relation between fractal dimension and screening efficiency is presented by using the box-counting method. In this research, we skilfully transform the tough problem of image detection for particle size distribution into the calculation of the fractal dimension of the coal-edge image, and closely associate the fractal dimension with screening efficiency. With this method, it will be easier to predict the screening efficiency in real-time.
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Han, Guoqing, Shuang Yan, Zejie Chen, and Lin Chen. "Convolutional neural network microseismic event detection based on variance fractal dimension." Journal of Physics: Conference Series 2196, no. 1 (2022): 012016. http://dx.doi.org/10.1088/1742-6596/2196/1/012016.
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Abstract Microseismic event detection helps to predict outbreak catastrophic problems and has essential applications in resource exploration. Low SNR microseismic signal detection is a challenging task in microseismic detection. In this paper, we propose a (convolutional neural network microseismic detection method based on variance fractal dimension) VFD-CNN method based on the variance fractal dimension (VFD). In this method, signals and background noise are first measured by variance fractal dimension, which can effectively extract seismic nonlinear features. These fractal features are then fed into VFD-CNN to distinguish signal and noise. Finally, the variance fractal dimension of the test data is fed into the optimal model to detect microseismic events. The VFD-CNN method can significantly improve the detection capability of low SNR microseismic signals. To verify the performance of the VFD-CNN method, We use the VFD-CNN method to synthesize microseismic data. Furthermore, the comparison experiments were conducted using VFD-CNN and short-term averaging to long-term averaging (STA/LTA) algorithms. The results show that the VFD-CNN method can significantly improve the detection of low SNR microseismic signals, and its precision is substantially higher than the STA/LTA algorithm.
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Tolіupa, Serhii, and Serhii Laptiev. "IMPROVING THE METHOD OF DETECTING RADIO SIGNALS USING TOPOLOGICAL THREAT IDENTIFICATION." Information systems and technologies security, no. 1 (7) (2024): 62–68. http://dx.doi.org/10.17721/ists.2024.7.62-68.
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Background. Humanity has entered the era of information value. An era in which information becomes a more important resource than other resources. Therefore, access to information, especially to conference information, especially to information that contains the main competitive advantages, is the primary task of competitive intelligence. Obtaining such information is most often associated with breaking the law and using special technical means. Complex data breaches can now be resolved at a faster pace, but tech intelligence professionals can use new ways to penetrate your system to steal valuable information and cause irreparable damage. Methods. The work examines the methods of detecting dangerous radio signals, which can be signals from radio jamming devices. An improved method of detecting radio signals is proposed, the improvement is based on the use of topological identification of threats. The method is based on the fact that the object of information activity must be divided into fractals, that is, areas of self similarity. Areas of self-similarity are determined by the physical properties of radio signals. In addition to the fractal dimension, in order to significantly increase the probability of detecting dangerous radio signals, introduce the appropriate fractal measures as the identification of radio signals. Fractal measures additionally allow you to determine the zones in which dangerous radio signals are detected. By setting the appropriate reference values of the relevant parameters of radio signals, it is possible to determine the fractal dimensions through the Hurst index and, depending on the obtained dimension value, it is possible to identify dangerous radio signals, signals of radio interceptor devices or other information capture devices that are installed at the facility with conference information. The use of the proposed method allows to increase the efficiency of detection of signals of radio interceptor devices and, in case of detection of such signals, to block the channel of information leakage. Results. According to research by analysts, 76% of international companies and government institutions have encountered industrial intelligence. With the help of technical means, 80-90% of the necessary information is extracted. In this regard, keeping commercially important information secret allows us to successfully compete in the market for production and sales of goods and services. The attacker uses technical means to gain access to commercial information. One of the types of technical means is a radio device. For the transmission of information, a transmission channel of the received information is created. Therefore, the issue of identifying and blocking information leakage channels is very important. Conclusions. It is proved that using the methods of fractal geometry, it is possible to divide the object of information activity into fractals, into areas of self-similarity. The criteria by which these areas are determined are determined by the physical principles of embedded devices. By setting the corresponding reference values of the corresponding parameters, it is possible to determine the fractal dimensions through the Hurst index.
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Ding, Zhe Feng. "Anti-Jamming Performance Simulation of PN-LFM Combined Ranging System." Applied Mechanics and Materials 462-463 (November 2013): 159–64. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.159.
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The pseudo-random binary-phase code phase modulation and linear frequency modulation (PN-LFM) combined ranging system is a new detection system, which has proven to be powerful capacity in range resolution, velocity resolution and maximum range measurable without ambiguity. In this paper, we study the anti-jamming performance of the new ranging system, especially in the fractal stochastic noise environments. We analyze the impact mechanism of fractal noise on the PN-LFM detection system, and simulate the output of correlating detection. The cross correlation function, power spectrum function and average power of pseudo random signal and fractal noise are deduced, compared with the impact of white noise on the pseudo code detection system. Simulation shows that PN-LFM combined ranging system possess powerful anti-jamming capacity, and performs better in the fractal noise environment than Pseudo random code phase modulation system.
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Sanchez-Montero, Rocio, Juan-Antonio Martinez-Rojas, Pablo-Luis Lopez-Espi, Luis Nuñez-Martin, and Efren Diez-Jimenez. "Filtering of Mammograms Based on Convolution with Directional Fractal Masks to Enhance Microcalcifications." Applied Sciences 9, no. 6 (2019): 1194. http://dx.doi.org/10.3390/app9061194.
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The image processing of mammograms is very important for the early detection of breast pathologies, including cancer. This paper proposes a new technique based on directional fractal filtering for detecting microcalcification clusters or irregularly shaped microcalcifications. The proposed algorithm has two parts: a preprocessing step for detecting and locating microcalcification; and a second zooming, enhancement, and segmentation step. Detection is performed by image convolution using a set of masks with interesting fractal properties. Combined with other simple mathematical operations, remarkable contrast enhancement and segmentation are produced. The final result permits the clear delineation of the shape of individual microcalcifications. A comparison is made with other microcalcification enhancement techniques described in the literature.
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Verhoeven, J. T. M., and J. M. Thijssen. "Potential of Fractal Analysis for Lesion Detection in Echographic Images." Ultrasonic Imaging 15, no. 4 (1993): 304–23. http://dx.doi.org/10.1177/016173469301500403.
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The application of fractal analysis to parametric imaging of B-mode echograms and to differentation of echographic speckle textures was investigated. Echograms were obtained from realistic simulations and from a clinical study on diffuse liver disease. The simulations comprised tissue models with randomly positioned scatterers in a 3-D volume in which the number density was varied over a range from 0.5 to 25 mm–3. The clinical echograms comprised both normals and patients with liver cirrhosis. Three methods of estimating the fractal dimension were investigated, two in the spatial image domain and one in the spatial frequency domain. The results of these methods are compared and the applicability and the limitations of texture differentiation using fractal analysis is discussed. The main conclusion is that fractal analysis offers no obvious advantage over statistical analysis of the texture of echographic images. Its use for parametric imaging is further limited by the need to use relatively large windows for local estimation of the fractal dimension.
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Abdikerimova, Gulzira, Ainur Shekerbek, Murat Tulenbayev, et al. "Detection of lung pathology using the fractal method." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6778. http://dx.doi.org/10.11591/ijece.v13i6.pp6778-6786.
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<span lang="EN-US">Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis. </span>
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Gulzira, Abdikerimova, Shekerbek Ainur, Tulenbayev Murat, et al. "Detection of lung pathology using the fractal method." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6778–86. https://doi.org/10.11591/ijece.v13i6.pp6778-6786.
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Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis.
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Liu, Yanbing, Bei Zhou, and Xinghua Yang. "Wavelet Analysis-Based Texture Analysis of Ceramic Surface Images." Advances in Mathematical Physics 2021 (October 31, 2021): 1–11. http://dx.doi.org/10.1155/2021/1745135.
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This paper is conducted to explore a new characterization method as a supplement to the traditional roughness characterization. The main research includes the extraction and evaluation of damage features of ceramic surface morphology by applying wavelet methods, the extraction of damage features in surface contours by using wavelet analysis, and the quantitative evaluation of damage degree by using damage rate and damage mean spacing. By comparing various fractal dimension calculation methods, a fractal dimension method suitable for calculating the ceramic surface was selected, and the fractal method was used to describe the ceramic surface topography as a whole. By comparing different methods of calculating the fractal dimension and further verifying them with the measured three-dimensional morphology, it is found that the vibrational method is more suitable for calculating the fractal dimension of ceramic surface, and its calculation accuracy is investigated, and the results show that the method is a reliable one. Based on the fractal theory, a mathematical model of surface wear and surface sealing was established. Further study of the model shows that the surface with a large fractal dimension has a good sealing effect; the surface corresponding to the best fractal dimension is the most resistant to wear. The fractal method can characterize the complexity of the surface profile as a whole. The wavelet method can describe the ceramic surface profile from a local perspective, and the combination of the two methods can characterize the ceramic surface well. Finally, the experimental device of the ceramic surface defect detection system is constructed, and the joint debugging of hardware and software is completed. Under different light source intensities, ceramic image samples are collected, and the accuracy detection experiments of sample defective edges are conducted, and the results show that the light source has a small impact on the accuracy of ceramic defective edge detection. The results show that the light source has more influence on the accuracy of scratch detection. The results show that the system constructed in this thesis has good applicability for different ceramic sample detection.
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Wang, Zhi Qiang, Gui Ying Zhang, and Bin Liu. "Detection of Wear Condition of Micro Milling Cutters Based on Length Fractal Dimension." Applied Mechanics and Materials 577 (July 2014): 697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.577.697.
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In this paper, a new method to realize online wear detection of micro-milling cutters based on length fractal dimension is proposed. On the basis of expression derivation of length fractal dimension, experiments are conducted. First, several cutters with different wear condition are chosen as reference samples. Their multi-section vibration signals in time-domain are collected and the clustering domain δ of each sample are obtained based on length fractal dimensions. Then, the vibration signals of tested cutters are monitored and analysed in time domain, thus their length fractal dimension are abstracted. Comparing the length fractal dimension of tested cutters with the clustering domain δ of reference samples, the wear condition of tested cutters are detected. The experimental results show that the length fractal dimension of each tested cutter falls in the clustering domain corresponding to the actual wear condition.
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Li, Yan Hong. "Based on the Fractal Technology of Bad Data Detection Method in Power System." Advanced Materials Research 490-495 (March 2012): 1358–61. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1358.
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detection and identification of bad data is an important part of state estimation in power system. To solute the problem generates a variety of detection methods and means in academic and industrial circles, commonly used methods include objective function detection, weighted residual detection, measurement suddenly-change detection and the comprehensive application of above methods. In order to detection the bad data from large amounts of data over the multiple sliding windows, bad data detection algorithm is proposed based on fractal technology building monotonic search space. Firstly, it gives the data set on the piecewise fractal model, and then based on this model to design a detection algorithm. The algorithm can reduce detection processing time greatly. The subsection fractal model can accurately model on the data self similarity and compress data. Theoretical analysis and experimental results show that, the algorithm has higher precision and lower time / space complexity, more suitable for bad data detection.
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He, Yue Mei, Bao Min Wang, and De Jun Qiao. "Application in Anomaly Detection of Network Traffic Based on Fractal Technology." Applied Mechanics and Materials 195-196 (August 2012): 987–91. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.987.
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Fractal model is applicable to describe the complex shapes of nature. Self-similarity is the basic feature of fractal. Anomaly detection of network traffic is the key to security and reliability of network. In this paper, a new algorithm for anomaly detection of network traffic based on fractal technology is proposed. It can achieve higher precision with less space and time complexity. Theoretical analysis and experiments show that the method can discover the abnormal network traffic accurately and efficiently.
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Elkington, Liam, Prakash Adhikari, and Prabhakar Pradhan. "Fractal Dimension Analysis to Detect the Progress of Cancer Using Transmission Optical Microscopy." Biophysica 2, no. 1 (2022): 59–69. http://dx.doi.org/10.3390/biophysica2010005.
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Fractal dimension, a measure of self-similarity in a structure, is a powerful physical parameter for the characterization of structural property of many partially filled disordered materials. Biological tissues are fractal in nature and reports show a change in self-similarity associated with the progress of cancer, resulting in changes in their fractal dimensions. Here, we report that fractal dimension measurement is a potential technique for the detection of different stages of cancer using transmission optical microscopy. Transmission optical microscopy of a thin tissue sample produces intensity distribution patterns proportional to its refractive index pattern, representing its mass density distribution. We measure fractal dimension detection of different cancer stages and find its universal feature. Many deadly cancers are difficult to detect in their early to different stages due to the hard-to-reach location of the organ and/or lack of symptoms until very late stages. To study these deadly cancers, tissue microarray (TMA) samples containing different stages of cancers are analyzed for pancreatic, breast, colon, and prostate cancers. The fractal dimension method correctly differentiates cancer stages in progressive cancer, raising possibilities for a physics-based accurate diagnosis method for cancer detection.
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MA ZHAO-MIAN and TAO CHUN-KAN. "REGIONAL FRACTAL AND ARTIFICIAL OBJECT DETECTION." Acta Physica Sinica 48, no. 12 (1999): 2202. http://dx.doi.org/10.7498/aps.48.2202.
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Tao, Dong Wang, Dong Yu Zhang, and Hui Li. "Structural Seismic Damage Detection Using Fractal Dimension of Time-Frequency Feature." Key Engineering Materials 558 (June 2013): 554–60. http://dx.doi.org/10.4028/www.scientific.net/kem.558.554.
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In this paper, a data-driven approach to localizing structural damage subjected to ground motion is proposed by using the fractal dimension of the time-frequency features of structural dynamic responses. The time-frequency feature is defined as the real part of wavelet coefficient and the fractal dimension adopts the box-counting method. It is shown that the proposed fractal dimensions at each story of linear system are identical, while the fractal dimension at the stories with nonlinearity is different from those at the stories with linearity. Therefore, the nonlinear behavior of structural damage caused by strong ground motions can be detected and localized through comparing the fractal dimensions of structural responses at different stories. Shaking table test on a uniform 16-story 3-bay steel frame with added friction dampers modelling interstory nonlinear behavior was conducted. The experiment results validate the effectiveness of the proposed method to localize single and multi seismic damage of structures.
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An, Baizhou, Zhaofa Zeng, Zhaotao Yan, et al. "A Novel Approach to Edge Detection for a Gravity Anomaly Based on Fractal Surface Variance Statistics of Fractal Geometry." Applied Sciences 12, no. 16 (2022): 8172. http://dx.doi.org/10.3390/app12168172.
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Fractal geometry has developed rapidly, and is widely used in various disciplines. However, only a few fractal dimension methods and techniques have been applied to the processing of gravity data, especially in the detection of geological edges and interfaces. In this paper, the definition, properties and characteristics of fractal dimensions are used to improve the edge detection of gravity anomalies, and a theoretical gravity model is established. At the same time, a new method of fractal surface variance statistics is applied and compared with traditional methods. The fractal gravity anomaly processing methods in different directions are analyzed, and the results show that the maximum value of the fractal surface variance statistical method on a fixed window can be used to delineate the geological edge of the ore body. When the method in this paper is applied to the Luobusha chromite deposit in Tibet, China, the fractal dimension corresponds well with the structural development zones of various faults, and it is also helpful to delineate the boundary of the chromite deposit and identify the interface with an obvious difference in gravity anomaly density.
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Qu, Chang, Meng Xu, Jun Ze Wang, and Jie Deng. "Applications of Fractal in Textile Engineering." Advanced Materials Research 627 (December 2012): 567–71. http://dx.doi.org/10.4028/www.scientific.net/amr.627.567.
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In view of the generation of fractal images, the applications of fractal in textile engineering are summarized into two parts. Firstly, fractal images are used in textile image design, textile pattern design and so on. Secondly, fabric properties, such as woven fabric permeability analysis, fabric defect detection, texture analysis of the fabric surface and so on, are analyzed based on fractal theory. The applications of fractal images provide some new creative ideas for textile pattern design. The fractal theory is a powerful tool to solve the complex problems of textile engineering.
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Li, Jian, Changkui Cheng, Lianwei Bao, and Tianyan Jiang. "Resonant Frequency Calculation and Optimal Design of Peano Fractal Antenna for Partial Discharge Detection." International Journal of Antennas and Propagation 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/361517.
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Ultra-high-frequency (UHF) approaches have caught increasing attention recently and have been considered as a promising technology for online monitoring partial discharge (PD) signals. This paper presents a Peano fractal antenna for UHF PD online monitoring of transformer with small size and multiband. The approximate formula for calculating the first resonant frequency of the Peano fractal antenna is presented. The results show that the first resonant frequency of the Peano fractal antenna is smaller than the Hilbert fractal antenna when the outer dimensions are equivalent approximately. The optimal geometric parameters of the antenna were obtained through simulation. Actual PD experiments had been carried out for two typically artificial insulation defect models, while the proposed antenna and the existing Hilbert antenna were both used for the PD measurement. The experimental results show that Peano fractal antenna is qualified for PD online UHF monitoring and a little more suitable than the Hilbert fractal antenna for pattern recognition by analyzing the waveforms of detected UHF PD signals.
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Li, B., P.-L. Zhang, S.-S. Mi, Y.-T. Zhang, and D.-S. Liu. "Multi-scale fractal dimension based on morphological covering for gear fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 9 (2011): 2242–49. http://dx.doi.org/10.1177/0954406211405913.
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Fractal dimension (FD) is one of the most utilized parameters for characterizing and discriminating vibration signals in gear fault detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant FD at all scales may not be appropriate. Motivated by this fact, this article explores the capacity of the multi-scale fractal dimension (MFD) to represent the complexity of vibration signals for gear fault diagnosis. We select the morphological covering method to calculate the MFD. Vibration signals measured from a gear test rig with five states are employed to evaluate the effectiveness of the presented method. Experimental results reveal that the vibration signals acquired from gear with five states demonstrate different fractal structures when the visualization scales are changed. The MFD can provide more information about the signals and yield a higher classification rate than the FD and traditional statistical parameters. It is very reasonable to apply the MFD to vibration signal analysis for improving the performance of the gear fault diagnosis.
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Ugwu, Cynthia I., Emanuele Caruso, and Oswald Lanz. "Fractals as Pre-Training Datasets for Anomaly Detection and Localization." Fractal and Fractional 8, no. 11 (2024): 661. http://dx.doi.org/10.3390/fractalfract8110661.
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Anomaly detection is crucial in large-scale industrial manufacturing as it helps to detect and localize defective parts. Pre-training feature extractors on large-scale datasets is a popular approach for this task. Stringent data security, privacy regulations, high costs, and long acquisition time hinder the development of large-scale datasets for training and benchmarking. Despite recent work focusing primarily on the development of new anomaly detection methods based on such extractors, not much attention has been paid to the importance of the data used for pre-training. This study compares representative models pre-trained with fractal images against those pre-trained with ImageNet, without subsequent task-specific fine-tuning. We evaluated the performance of eleven state-of-the-art methods on MVTecAD, MVTec LOCO AD, and VisA, well-known benchmark datasets inspired by real-world industrial inspection scenarios. Further, we propose a novel method to create a dataset by combining the dynamically generated fractal images creating a “Multi-Formula” dataset. Even though pre-training with ImageNet leads to better results, fractals can achieve close performance to ImageNet under proper parametrization. This opens up the possibility for a new research direction where feature extractors could be trained on synthetically generated abstract datasets mitigating the ever-increasing demand for data in machine learning while circumventing privacy and security concerns.
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Tosi, Patrizia, Salvatore Barba, Valerio De Rubeis, and Francesca Di Luccio. "Seismic signal detection by fractal dimension analysis." Bulletin of the Seismological Society of America 89, no. 4 (1999): 970–77. http://dx.doi.org/10.1785/bssa0890040970.
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Abstract We introduce a new detection algorithm with improved local and regional seismic signal recognition. The method is based on the difference between seismic signals and background random noise in terms of fractal dimension D. We compare the new method extensively with standard methods currently in use at the Seismic Network of the Istituto Nazionale di Geofisica. Results from the comparisons show that the new method recognizes seismic phases detected by existing procedures, and in addition, it features a greater sensitivity to smaller signals, without an increase in the number of false alarms. The new method was tested on real continuous data and artificially simulated high-noise conditions and demonstrated a capability to recognize seismic signals in the presence of high noise. The efficiency of the method is due to a radically different approach to the topic, in that the assertion that a signal is fractal implies a relationship between the spectral amplitude of different frequencies. This relationship allows, for the fractal detector, a complete analysis of the entire frequency range under consideration.
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Kirichenko, Lyudmyla, Tamara Radivilova, and Vitalii Bulakh. "Machine Learning in Classification Time Series with Fractal Properties." Data 4, no. 1 (2018): 5. http://dx.doi.org/10.3390/data4010005.
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The article presents a novel method of fractal time series classification by meta-algorithms based on decision trees. The classification objects are fractal time series. For modeling, binomial stochastic cascade processes are chosen. Each class that was singled out unites model time series with the same fractal properties. Numerical experiments demonstrate that the best results are obtained by the random forest method with regression trees. A comparative analysis of the classification approaches, based on the random forest method, and traditional estimation of self-similarity degree are performed. The results show the advantage of machine learning methods over traditional time series evaluation. The results were used for detecting denial-of-service (DDoS) attacks and demonstrated a high probability of detection.
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Li, Yan Hong, Tian Dong Yu, and Shu Liang Li. "Based on the Fractal Technology of Harmonic Detection Method in Power System." Advanced Materials Research 503-504 (April 2012): 1593–96. http://dx.doi.org/10.4028/www.scientific.net/amr.503-504.1593.
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The harmonic pollution is one of the serious problems which impact on power system security and stability. With the development of power electronic technology, harmonic problems become increasingly prominent. Harmonic detection is an important content in the harmonic problems, is the base to solve the problem of harmonic. The harmonic of power system have characteristics of random and nonstationary, not easy to detect, in recent years, a variety of detection methods emerge in an endless stream, this paper introduces a technique of detecting harmonics in power system by using fractal method, this method by calculating the dynamic waveform grid fractal dimension, setting threshold value, and determines the waveform distortion occurring time. After the theoretical analysis and practical measurement, this method can be effectively used on harmonic detection and can meet the requirements in speed and accuracy.
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Huang, Haoran, and Xiaochuan Luo. "A Holistic Approach to IGBT Board Surface Fractal Object Detection Based on the Multi-Head Model." Machines 10, no. 8 (2022): 713. http://dx.doi.org/10.3390/machines10080713.
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In industrial visual inspection, foreign matters are mostly fractal objects. Detailed detection of fractal objects is difficult but necessary because better decision-making relies on more detailed and more comprehensive detection information. This presents a challenge for industrial applications. To solve this problem, we proposed a holistic approach to fractal object detection based on a multi-head model. We proposed the IWS (Information Watch and Study) module to provide enhancement learning capabilities for object information. It increases the detection dimension of the object and can perform more detailed detection. In order to realize the portability of the IWS module, it can be easily and quickly deployed to the existing advanced object detection model to achieve end-to-end detection. We proposed the FGI (Fine-Grained Information) Head, which is used to extract more comprehensive feature vectors from the original base model. We proposed the WST (Watch and Study Tactic) Learner for object information processing and adaptive learning of class cluster centers. Using the MRD (Multi-task Result Determination) strategy to combine the classification results and IWS results, the final detection results are derived. In the experiment, the IWS and MRD were mounted on three different models of the YOLO series. The experimental results show that YOLO+IWS has good foreign object detection capabilities to meet the needs of industrial visual inspection. Moreover, for the detailed detection ability of fractal objects, YOLO+IWS is better than the other 11 competing methods. We designed a new evaluation index and an adjustment mechanism of class learning weights to make better judgments and more balanced learning. Not only that, we applied YOLO+IWS to form a brand new object detection system.
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LEGAT, ANDRAŽ, and EDVARD GOVEKAR. "DETECTION OF CORROSION BY ANALYSIS OF ELECTROCHEMICAL NOISE." Fractals 02, no. 02 (1994): 241–44. http://dx.doi.org/10.1142/s0218348x94000259.
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Stochastic fluctuations of the corrosion potential and the current generated by corrosion reactions are known as electrochemical noise. These fluctuations can be measured in freely corroding systems, therefore the characteristics of electrochemical noise are influenced only by the type and rate of corrosion. The classical spectral analysis of electrochemical noise in the frequency domain achieve good correlation to corrosion rate and type; however, the chaotic nature of corrosion processes requires different mathematical treatment. In this paper self-similarity and fractal dimensions of electrochemical noise are examined in order to explain its mechanism and improve the corrosion monitoring system. Capacity and correlation fractal dimensions of voltage and current-noise, measured on various metals, are calculated and compared to the results of the classical spectral analysis. Relations between different rates and types of corrosion (passivation, local, uniform) and the fractal characteristics of electrochemical noise are established. The analysis of spontaneous electrochemical voltage and current fluctuations is confirmed as a rich source of information in corrosion processes.
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Zhang, Yanli, Weidong Zhou, and Shasha Yuan. "Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG." International Journal of Neural Systems 25, no. 06 (2015): 1550020. http://dx.doi.org/10.1142/s0129065715500203.
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Automatic seizure detection technology is of great significance for long-term electroencephalogram (EEG) monitoring of epilepsy patients. The aim of this work is to develop a seizure detection system with high accuracy. The proposed system was mainly based on multifractal analysis, which describes the local singular behavior of fractal objects and characterizes the multifractal structure using a continuous spectrum. Compared with computing the single fractal dimension, multifractal analysis can provide a better description on the transient behavior of EEG fractal time series during the evolvement from interictal stage to seizures. Thus both interictal EEG and ictal EEG were analyzed by multifractal formalism and their differences in the multifractal features were used to distinguish the two class of EEG and detect seizures. In the proposed detection system, eight features (α0, αmin, αmax, Δα, f(α min ), f(α max ), Δf and R) were extracted from the multifractal spectrums of the preprocessed EEG to construct feature vectors. Subsequently, relevance vector machine (RVM) was applied for EEG patterns classification, and a series of post-processing operations were used to increase the accuracy and reduce false detections. Both epoch-based and event-based evaluation methods were performed to appraise the system's performance on the EEG recordings of 21 patients in the Freiburg database. The epoch-based sensitivity of 92.94% and specificity of 97.47% were achieved, and the proposed system obtained a sensitivity of 92.06% with a false detection rate of 0.34/h in event-based performance assessment.
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Luo, Yuan, Chao Ji, Yi Zhang, and Zhang Fang Hu. "A Fractal Based Subpixel Image Edge Detection Algorithm." Applied Mechanics and Materials 239-240 (December 2012): 1546–51. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1546.
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Application of machine vision method for MEMS dynamic parameters were measured, the testing image have a certain degree of ambiguity.This paper presents a sub-pixel algorithm based on fractal and wavelet transform: Firstly, using self-similar characteristics of fractal interpolation to overcome the problem ,that can not be accurate interpolation and the edge of the image reconstruction. Then because of abilities of high resolution and anti-noise,using wavelet transform modulus maxima,the image edge detection.The experimental results show that the algorithm can reach 0.02 pixel accuracy.
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Hadjileontiadis, L. J., and E. Douka. "Crack detection in plates using fractal dimension." Engineering Structures 29, no. 7 (2007): 1612–25. http://dx.doi.org/10.1016/j.engstruct.2006.09.016.
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Bai, Run Bo, Zong Mei Xu, and Xiu Mei Qiu. "Damage Detection in Beam-Type Structures Using Fractal Dimension Trajectory of Rotated Higher Vibration Modes." Applied Mechanics and Materials 275-277 (January 2013): 1111–17. http://dx.doi.org/10.4028/www.scientific.net/amm.275-277.1111.
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Damage-induced local response is probably easy to be captured by the higher modes of the structures, especially for the small defects. The aim of this paper is to overcome the inherent deficiency of fractal dimension to identify crack when implemented to higher mode shapes. The proposed approach reconstructs the higher mode shape through rotation transformation, and then the fractal dimension analysis is implemented on this new mode shape to yield a fractal dimension trajectory. The location of the crack can be determined by the sudden peaks at the fractal dimension trajectory. The applicability and effectiveness of the proposed method is validated by using numerical simulations on damage identification of a cracked cantilever beam.
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Jakhar, Shyo Prakash, Amita Nandal, Arvind Dhaka, Adi Alhudhaif, and Kemal Polat. "Brain tumor detection with multi-scale fractal feature network and fractal residual learning." Applied Soft Computing 153 (March 2024): 111284. http://dx.doi.org/10.1016/j.asoc.2024.111284.
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Tahir, Ahsen, Gordon Morison, Dawn A. Skelton, and Ryan M. Gibson. "Hardware/Software Co-Design of Fractal Features Based Fall Detection System." Sensors 20, no. 8 (2020): 2322. http://dx.doi.org/10.3390/s20082322.
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Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software co-design approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%.
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Wiharto, Suryani Esti, and Y. Kipti Muhammad. "Assessment of Early Hypertensive Retinopathy using Fractal Analysis of Retinal Fundus Image." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 1 (2018): 445–54. https://doi.org/10.12928/TELKOMNIKA.v16i1.6188.
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Hypertensive retinopathy is characterized by changes in retinal vessels, a change known as tortuosity. Automated analysis of retinal vascular changes will make it easier for clinicians to make an initial diagnosis of the disease. The pattern of blood vessels in the retina of the eye can be approached with a fractal pattern. This study proposes a method for the early detection of disease hypertensive retinopathy using the fractal analysis approach fundus retinal image. Variable fractal used is the fractal dimension and lacunarity, whereas for the classification algorithm using ensemble Random Forest and validation using the k-fold cross-validation. Performance measurement using the parameters of accuracy, positive prediction value (PPV), negative prediction value (NPV), sensitivity, specificity and area under the curve (AUC). The test results using 10-fold cross-validation values obtained accuracy 88.0%, PPV 84.0%, NPV 92.0%, sensitivity 91.3%, specificity 85.19%, and 88.25% AUC. The performance is produced when using lacunarity the box size 22 . Based on the research results, it can be concluded that early detection of hypertensive retinopathy with fractal analysis approaches have a performance based on AUC produced included in good categories.
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Yoder, Keith J., Geoffrey Brookshire, Ryan M. Glatt, et al. "Fractal Dimension Distributions of Resting-State Electroencephalography (EEG) Improve Detection of Dementia and Alzheimer’s Disease Compared to Traditional Fractal Analysis." Clinical and Translational Neuroscience 8, no. 3 (2024): 27. http://dx.doi.org/10.3390/ctn8030027.
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Across many resting-state electroencephalography (EEG) studies, dementia is associated with changes to the power spectrum and fractal dimension. Here, we describe a novel method to examine changes in the fractal dimension over time and within frequency bands. This method, which we call fractal dimension distributions (FDD), combines spectral and complexity information. In this study, we illustrate this new method by applying it to resting-state EEG data recorded from patients with subjective cognitive impairment (SCI) or dementia. We compared the performance of FDD with the performance of standard fractal dimension metrics (Higuchi and Katz FD). FDD revealed larger group differences detectable at greater numbers of EEG recording sites. Moreover, linear models using FDD features had lower AIC and higher R2 than models using standard full time-course measures of the fractal dimension. FDD metrics also outperformed the full time-course metrics when comparing SCI with a subset of dementia patients diagnosed with Alzheimer’s disease. FDD offers unique information beyond traditional full time-course fractal analyses and may help to identify dementia caused by Alzheimer’s disease and dementia from other causes.
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PEROV, ROMAN A., OLEG S. LAUTA, ALEXANDER M. KRIBEL, and YURI V. FEDULOV. "A METHOD FOR DETECTING ANOMALIES IN NETWORK TRAFFIC." H&ES Research 14, no. 3 (2022): 25–31. http://dx.doi.org/10.36724/2409-5419-2022-14-3-25-31.
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Introduction. Computer networks (CN) are highly developed systems with a multi-level hierarchical structure. The use of information and communication technologies in the CN to collect information allows an attacker to influence networks through cyber-attacks. This is facilitated by the massive use of outdated operating systems, ineffective protection mechanisms and the presence of multiple vulnerabilities in unsecured network protocols. Such vulnerabilities help a potential attacker to change the settings of network devices, listen and redirect traffic, block network interaction and gain unauthorized access to the internal components of the CN. The impact of cyber-attacks leads to the appearance of abnormal traffic activity in the CN. For its constant monitoring and detection in the CN, it is necessary to take into account the presence of a large number of network routes, on which sharp fluctuations in data transmission delays and large packet losses periodically occur, new properties of network traffic appear, which requires ensuring high quality of application service. All this served as an incentive to search for new methods of detecting and predicting cyber-attacks fractal analysis can also be attributed to them. The aim of the work is to develop a conceptual method for detecting anomalies caused by cyber-attacks in network traffic through the use of fractal analysis. Methods used. The main provisions of the fractal theory and the use of self-similarity assessment methods proposed by this theory, such as the extended Dickey-Fuller test, R/S analysis and the DFA method, are applied. When testing fractal methods that allow conducting studies of long-term dependencies in network traffic. The scientific novelty lies in the fact that the proposed method correctly identifies anomalies caused by the impact of cyber-attacks, and also allows you to predict and detect both known and unknown computer attacks at an early stage of their manifestation. Practical significance. The presented methodology can be used as an early detection system for cyber-attacks, based on the detection of anomalies in network traffic and the adoption of effective measures to protect the network.
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Qu, Yongjie, Yang Wu, Guoyu Hei, Da Teng, and Xinli Yan. "Hilbert Fractal Antenna for Abnormal Discharge Detection of Transmission Equipment." Journal of Physics: Conference Series 2584, no. 1 (2023): 012082. http://dx.doi.org/10.1088/1742-6596/2584/1/012082.
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Abstract The safe and stable operation of the power system can ensure people’s basic electricity needs. It also plays an important role in energy security and economic development and can contribute to environmental protection and energy conservation. If there are problems in the power system, it will not only affect people’s normal life and work but also lead to power loss or waste, thereby exacerbating the impact on the environment. With the long-term operation of transmission equipment, insulation devices are aging, leading to frequent abnormal discharges and affecting the safety and stability of the power system. Therefore, timely detection and troubleshooting are necessary. When abnormal discharge occurs in transmission equipment, it will radiate radio frequency (RF) electromagnetic waves, which are mainly concentrated between 200 MHz and 300 MHz. RF antennas that can capture signals in this frequency band can be used for detection to determine whether abnormal discharge has occurred. This article uses the Hilbert fractal antenna as the research object to analyze the impact of different orders on antenna performance and optimizes the different feeding point positions of the antenna. Finally, the antenna structure for detecting abnormal discharge is obtained. By making an actual antenna and conducting experimental analysis, the discharge detection function of the designed antenna is verified.