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Research on resilience model of UAV swarm based on complex network dynamics

Wei Kunlun, Zhang Tao, Chuanfu Zhang

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 4

art. no. 173125

EN

Unmanned Aerial Vehicle (UAV) swarms are utilized in various missions and operated within an open environment that is prone to disruptions. The resilience of UAV swarms, an important requirement, mainly revolves around ensuring stable and uninterrupted operations. Malicious attacks can implement the adverse impacts of potential threats through swarm communication links. In this context, the SIS (Susceptible → Infected → Susceptible) method is suitable for describing the information transmission within UAV swarms. An enhanced resilience model of the UAV swarm is proposed in this study, which incorporates the factors of self-dynamics, dynamics of topology, dynamics of information transmission, and SIS into the complex network model. Self-dynamics refer to the internal dynamics of the drones. In this paper, dynamics of topology consist of three factors: the varying distance between drones, the incoming degrees of each drone, and the number of communication types between drones. Lastly, dynamics of information transmission are characterized by SIS. The model proposed in this paper has the capability to effectively capture changes in the network topology as well as the dynamics of the system, which are significant contributors to the loss of resilience. And then, the average number of susceptible drones is utilized as the metric to evaluate the resilience of the swarm. Furthermore, an experiment is conducted where a UAV swarm successfully carries out a surveillance mission to demonstrate the advantages of our proposed method. The proposed model not only enables the support of mission planning but also facilitates the design enhancements of UAV swarms.

2

Wavelet-Hilbert transform based bidirectional least squares grey transform and modified binary grey wolf optimization for the identification of epileptic EEGs

Liu Chang, Chen Wanzhong, Zhang Tao

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 2

442--462

EN

Wavelet based seizure detection is an importance topic for epilepsy diagnosis via electroencephalogram (EEG), but its performance is closely related to the choice of wavelet bases. To overcome this issue, a fusion method of wavelet packet transformation (WPT), Hilbert transform based bidirectional least squares grey transform (HTBiLSGT), modified binary grey wolf optimization (MBGWO) and fuzzy K-Nearest Neighbor (FKNN) was proposed. The HTBiLSGTwas first proposed to model the envelope change of a signal, then WPT based HTBiLSGT was developed for EEG feature extraction by performing HTBiLSGT for each subband of each wavelet level. To select discriminative features, MBGWO was further put forward and employed to conduct feature selection, and the selected features were finally fed into FKNN for classification. The Bonn and CHB-MIT EEG datasets were used to verify the effectiveness of the proposed technique. Experimental results indicate the proposed WPT based HTBiLSGT, MBGWO and FKNN can respectively lead to the highest accuracies of 100% and 98.60 ± 1.35% for the ternary and quinary classification cases of Bonn dataset, it also results in the overall accuracy of 99.48 ± 0.61 for the CHB-MIT dataset, and the proposal is proven to be insensitive to the choice of wavelet bases.

3

Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)2PCA and KELM

Zhang Tao, Chen Wanzhong, Chen Xiaojuan

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

279--297

EN

In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)2PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating features in the WST domain, the BW(2D)2PCA was proposed based on original two-directional two-dimensional principal component analysis, by considering both the contribution of eigenvalue and the variation of two adjacent eigenvalues. Totally fifteen classification tasks of classifying normal vs interictal vs ictal EEGs, non-seizure vs seizure EEGs and normal vs congestive heart failure (CHF) ECGs were investigated. Applying patient non-specific strategy, the proposed scheme reported ACCs of no less than 99.300 ± 0.121 % for all the thirteen classification cases of Bonn dataset in classifying normal vs interictal vs ictal EEGs, MCC of 90.947 ± 0.128 % in distinguishing non-seizure vs seizure EEGs of CHB-MIT dataset, and MCC of 99.994 ± 0.001 % in identifying normal vs CHF ECGs of BBIH dataset. Experimental results indicate BW(2D)2PCA based framework outperforms (2D)2PCA based scheme, the high-performance results manifest the effectiveness of the proposed framework and our proposal is superior to most existing approaches.

4

Dynamic process and model simulation of soil water content during citrus growth period

Zhu Shijiang, Li Hu, Xu Wen, Zhang Tao, Liu Caihong, Peng Yuqiang

Acta Geophysica

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2023

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Vol. 71, no. 3

1525--1537

EN

The change of soil water content in crop root system is the basis for the designing of water-saving irrigation scheduling. In order to explore the dynamic changes of soil water content, our study carried out long-term monitoring on the soil content at different depths in a typical citrus orchard. In this concern, water contents, salinities, and nitrate concentrations were measured weakly and were compared with the model predictions. One of the important perspectives is the growth restricting factor in the soil water, yield, and fruit quality of citrus. Combined with the meteorological data, the changing characters of soil water content and its responses to evapotranspiration and precipitation during the whole growth period were investigated. And then the changing process of soil moisture was simulated through the soil water balance model. The results showed that during the whole growth period of the citrus, the soil water content buried at 40 cm depth had the best correlation with that in the main active layer of citrus root system (0–60 cm), between which the correlation coefficient reached 0.988. Therefore, the depth of 40 cm could be used as the representative depth of soil water content monitoring. Under the combined effects of precipitation and evapotranspiration, soil depth could influence the changing process of soil water content, of which the effect weakened with the increase in depth. The water balance model within 1-week timescale was appropriate to simulate the changing process of soil water content.

5

Identification of the heat source and thermal material model parameters for the laser engineered net shaping

Hajder Lucyna, Zhang Tao, Nguyen Vu

Computer Methods in Materials Science

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2022

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Vol. 22, No. 1

43--54

EN

The research’s primary goal is to identify the heat source and thermal material model parameters for the numerical simulation of the laser engineered net shaping (LENS). Inconel 718 was selected as a case study for the current investigation. The LENS process’s numerical model was developed within commercial finite element software and was used as a direct problem model during the parameter identification stage. Experimental data were obtained based on a rectangular-shaped sample with thermocouples located under the based material surface. The recorded thermal profiles were used to establish a goal function for the parameter identification stage. As a result, parameters describing the melt pool geometry during the additive manufacturing, as well as thermal coefficients describing interactions between the sample material and surrounding/base material, were determined.

6

Comparative analysis of cold and warm rolling on tensile properties and microstructure of additive manufactured Inconel 718

Zhang Tao, Li Huigui, Gong Hai, Wu Yunxin, Ahmad Abdulrahaman Shuaibu, Chen Xin, Zhang Xiaoyong

Archives of Civil and Mechanical Engineering

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2022

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Vol. 22, no. 1

art. no. e44, 2022

EN

Despite the high efficiency and low cost of wire + arc additive manufacture (WAAM), the epitaxial grown columnar dendrites of WAAM deposited Inconel 718 cause inferior properties and severe anisotropy compared to the wrought components. Fundamental studies on the influence of one-pass cold and warm rolling on hardness and microstructure were investigated. Then the interpass cold and warm rolling on tensile properties were also analyzed. The results show that the one-pass rolling increases the hardness and displays a heterogeneous hardness distribution compared to the as-deposited material, and the warm rolling exhibits a larger and deeper strain compared to cold rolling. The columnar dendrites gradually change to cell dendrites under the rolling process and then change to equiaxed grains with the subsequent new layer deposition. The average grain size is 16.8 μm and 23.5 μm for the warm and cold rolling, respectively. The strongly textured columnar dendrites with preferred < 001 > orientation transform to equiaxed grains with random orientation after rolling process. The grain refinement contributes to the dispersive distributed strengthening phases and the increase in its fraction with heat treatment. The as-deposited samples show superior tensile properties compared to the cast material but inferior compared to the wrought components, while the warm-rolled samples show superior tensile properties to wrought material. Isotropic tensile properties are obtained in warm rolling compared to cold rolling. The rolling process and heat treatment both decrease the elongation and lead to a transgranular ductile fracture mode. Finally, the rolling-induced strengthening mechanism was discussed.

7

Parkinson disease detection using energy direction features based on EMD from voice signal

Zhang Tao, Zhang Yajuan, Sun Hao, Shan Haoran

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

127--141

EN

Voice disorders are one of the incipient symptoms of Parkinson's disease (PD). Recent studies have shown that approximately 90% of PD patients suffer from vocal disorders. Therefore, it is significant to extract pathological information on the voice signals to detect PD. In this paper, a feature, named energy direction features based on empirical mode decomposition (EDF-EMD), is proposed to show the different characteristics of voice signals between PD patients and healthy subjects. Firstly, the intrinsic mode functions (IMFs) were obtained through the decomposition of voice signals by EMD. Then, the EDF is obtained by calculating the directional derivatives of the energy spectrum of each IMFs. Finally, the performance of the proposed feature is verified on two different datasets: dataset-Sakar and dataset-CPPDD. The proposed approach shows the best average resulting accuracy of 96.54% on dataset-Sakar and 92.59% on dataset-CPPDD. The results demonstrate that the method proposed in this paper is promising in the field of PD detection.

8

Complex-valued distribution entropy and its application for seizure detection

Zhang Tao, Chen Wanzhong, Li Mingyang

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

306--323

EN

Embedding entropies are powerful indicators in quantifying the complexity of signal, but most of them are only applicable for real-valued signal and the phase information is ignored if the analyzed signal is complex-valued. To assess the complexity of complex-valued signal, a new entropy called complex-valued distribution entropy (CVDistEn) was first proposed in this study. Two rules, namely equal width criterion and equal area criterion, were employed to demarcate the complex-valued space and two kinds of CVDistEn, i.e., CVDistEn1 and CVDistEn2 were raised. Furthermore, two novel feature extraction methods: (1) flexible analytic wavelet transform (FAWT)-based CVDistEn1 and logarithmic energy (LE) (FAWTC1L), (2) FAWT-based CVDistEn2 and LE (FAWTC2L) were subsequently put forward to characterize the interictal and ictal EEGs. Fuzzy k-nearest neighbors (FKNN) classifier was finally employed to classify these two types of EEGs automatically. Experiment results show the fusion method of FAWTC1L and FKNN leads to the best accuracies (ACCs)/Matthews correlation coefficients (MCCs) of 99.99%/99.97% and 100%/100% for Bonn and Neurology & Sleep Centre EEG datasets, respectively, while the other fusion scheme of FAWTC2L and FKNN results in the highest ACCs/MCCs of 99.97%/99.93% and 99.94%/99.89% for the same datasets. The proposed methods outperform other entropy-related seizure detection schemes and most of state-of-the-art techniques, they provide another new way for automated seizure detection in EEG.

9

Geomagnetic detection method for pipeline defects based on ceemdan and WEP-TEO

Zhang Tao, Wang Xinhua, Chen Yingchun, Shuai Yi, Ullah Zia, Ju Haiyang, Zhao Yizhen

Metrology and Measurement Systems

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2019

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Vol. 26, nr 2

345--361

EN

This paper presents a geomagnetic detection method for pipeline defects using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet energy product (WEP) - Teager energy operator (TEO), which improves detection accuracy and defect identification ability as encountering strong inference noise. The measured signal is first subtly decomposed via CEEMDAN into a series of intrinsic mode functions (IMFs), which are then distinguished by the Hurst exponent to reconstruct the filtered signal. Subsequently, the scale signals are obtained by using gradient calculation and discrete wavelet transform and are then fused by using WEP. Finally, TEO is implemented to enhance defect signal amplitude, completing geomagnetic detection of pipeline defects. The simulation results created by magnetic dipole in a noisy environment, indoor experiment results and field testing results certify that the proposed method outperforms ensemble empirical mode decomposition (EEMD)-gradient, EEMD-WEP-TEO, CEEMDAN-gradient in terms of detection deviation, peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR).

10

Defect recognition of buried pipeline based on approximate entropy and variational mode decomposition

Ju Haiyang, Wang Xinhua, Zhang Tao, Zhao Yizhen, Ullah Zia

Metrology and Measurement Systems

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2019

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Vol. 26, nr 4

739--755

EN

The study aimed to examine the use of Geomagnetic Anomaly Detection (GAD) to locate the buried ferromagnetic pipeline defects without exposing them. However, the accuracy of GAD is limited by the background noise. In the present work, we propose an approximate entropy noise suppression (AENS) method based on Variational Mode Decomposition (VMD) for detection of pipeline defects. The proposed method is capable of reconstructing the magnetic field signals and extracting weak anomaly signals that are submerged in the background noise, which was employed to construct an effective detector of anomalous signals. The internal parameters of VMD were optimized by the Scale–Space algorithm, and their anti-noise performance was compared. The results show that the proposed method can remove the background noise in high-noise background geomagnetic field environments. Experiments were carried out in our laboratory and evaluation results of inspection data were analysed; the feasibility of GAD is validated when used in the application to detection of buried pipeline defects.