Method of Constructing a Nonlinear Approximating Scheme of a Complex Signal: Application Pattern Recognition

Mandrikova, Oksana; Mandrikova, Bogdana; Rodomanskay, Anastasia

doi:10.3390/math9070737

Cited by 13 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results show that the application of the threshold T (Figure 3c) does not allow one to localize the features and to suppress noise in a signal completely. The results confirm the efficiency of the suggested method and agree with the results of the paper [46], in which it was shown that the application of adaptive thresholds allows one to obtain more accurate estimates in the case of data with features of a different structure and a high level of noise. The results also confirm the author's statement [42] that when signal energy is relatively low noise energy, optimal estimates give too small a threshold and its application does not make it possible to obtain satisfactory results.…”

Section: Identification Of the Model Disturbed Componentsupporting

confidence: 89%

Wavelet Model of Geomagnetic Field Variations and Its Application to Detect Short-Period Geomagnetic Anomalies

2022

Self Cite

View full text Add to dashboard Cite

Geomagnetic data analysis is an important basis for the investigation of the processes in the near-Earth space, Earth magnetosphere, and ionosphere. The negative impact of geomagnetic anomalies on modern technical objects and human health determine the applied significance of the investigation and requires the creation of effective methods for timely detection of the anomalies. Priory complicated structure of geomagnetic data makes their formalization and analysis difficult. This paper proposes a wavelet model for geomagnetic field variations. It describes characteristic changes and anomalies of different amplitude and duration. Numerical realization of the model provides the possibility to apply it in online analysis. We describe the process of model identification and show its efficiency in the detection of sudden, short-period geomagnetic anomalies occurring before and during magnetic storms. Raw second data of the Paratunka and Magadan observatories and post-processed minute data were used in the paper. The question of noise effect on the proposed model results was under consideration.

show abstract

Section: Identification Of the Model Disturbed Componentsupporting

confidence: 89%

Wavelet Model of Geomagnetic Field Variations and Its Application to Detect Short-Period Geomagnetic Anomalies

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The results in [25] showed the prospects of this approach for detection of multi-scale anomalies. Moreover, the possibility of application of both the autoencoder and the nonlinear approximating schemes was considered in the papers [24,25]. The investigations in [24,25] showed that the autoencoder is more effective for the detection of narrow-band anomalies, and the nonlinear approximating scheme is more effective for the detection of short-period different-scale anomalies.…”

Section: Introductionmentioning

confidence: 86%

“…In the same paper [24] the autoencoder efficiency was shown for anomaly detection based on the search for the change points in a system. For the first time, we considered application of nonlinear approximating schemes for detection of anomalies in CR variations in [25]. The results in [25] showed the prospects of this approach for detection of multi-scale anomalies.…”

Section: Introductionmentioning

confidence: 99%

“…For the first time, we considered application of nonlinear approximating schemes for detection of anomalies in CR variations in [25]. The results in [25] showed the prospects of this approach for detection of multi-scale anomalies. Moreover, the possibility of application of both the autoencoder and the nonlinear approximating schemes was considered in the papers [24,25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection

Mandrikova,

Mandrikova

2024

Mathematics

Self Cite

View full text Add to dashboard Cite

It is often difficult to describe natural time series due to implicit dependences and correlated noise. During anomalous natural processes, anomalous features appear in data. They have a nonstationary structure and do not allow us to apply traditional methods for time series modeling. In order to solve these problems, new models, adequately describing natural data, are required. A new hybrid model of a time series (HMTS) with a nonstationary structure is proposed in this paper. The HMTS has regular and anomalous components. The HMTS regular component is determined on the basis of an autoencoder neural network. To describe the HMTS anomalous component, an adaptive nonlinear approximating scheme (ANAS) is used on a wavelet basis. HMTS is considered in this investigation for the problem of neutron monitor data modeling and anomaly detection. Anomalies in neutron monitor data indicate negative factors in space weather. The timely detection of these factors is critically important. This investigation showed that the developed HMTS adequately describes neutron monitor data and has satisfactory results from the point of view of numeric performance. The MSE model values are close to 0 and errors are white Gaussian noise. In order to optimize the estimate of the HMTS anomalous component, the likelihood ratio test was applied. Moreover, the wavelet basis, giving the least losses during ANAS construction, was determined. Statistical modeling results showed that HMTS provides a high accuracy of anomaly detection. When the signal/noise ratio is 1.3 and anomaly durations are more than 60 counts, the probability of their detection is close to 90%. This is a high rate in the problem domain under consideration and provides solution reliability of the problem of anomaly detection in neutron monitor data. Moreover, the processing of data from several neutron monitor stations showed the high sensitivity of the HMTS. This shows the possibility to minimize the number of engaged stations, maintaining anomaly detection accuracy compared to the global survey method widely used in this field. This result is important as the continuous operation of neutron monitor stations is not always provided. Thus, the results show that the developed HMTS has the potential to address the problem of anomaly detection in neutron monitor data even when the number of operating stations is small. The proposed HMTS can help us to decrease the risks of the negative impact of space weather anomalies on human health and modern infrastructure.

show abstract

“…Данная работа является продолжением исследований [19 -21]. В статье для повышения эффективности метода, представленного в работе [20], предложена оптимизация нейронной сети на основе регуляризаторов. Используя апостериорный риск, предложен способ оценки порогов, определяющих наличие аномалий в данных.…”

Section: Introductionunclassified

A method for analyzing complex structured data with elements of machine learning

Mandrikova

2022

Computer Optics

View full text Add to dashboard Cite

A method for analyzing data of complex structure based on combining a wavelet transform and neural networks Autoencoder is proposed. The method allows you to research the data structure, detect abnormal changes of various shapes and durations, and suppress noise. The efficiency of the method is shown on the example of data from a network of neutron monitor stations. Neutron monitor data determine the intensity of secondary cosmic rays and are one of the key factors in space weather. The numerical implementation of the method allows it to be applied on-line, which is of interest in problems of analyzing environmental data and detecting catastrophic events.

show abstract

Method of Constructing a Nonlinear Approximating Scheme of a Complex Signal: Application Pattern Recognition

Cited by 13 publications

References 21 publications

Wavelet Model of Geomagnetic Field Variations and Its Application to Detect Short-Period Geomagnetic Anomalies

Wavelet Model of Geomagnetic Field Variations and Its Application to Detect Short-Period Geomagnetic Anomalies

Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection

A method for analyzing complex structured data with elements of machine learning

Contact Info

Product

Resources

About