Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks

Liu, Lei; Ma, Lifeng; Zhang, Jie; Bo, Yuming

doi:10.1080/00207721.2021.1872118

Cited by 161 publications

(44 citation statements)

References 44 publications

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“…It is easy to see that the minimization of the upper bound (15) can be obtained when the estimator parameter is selected as (22), and the proof is complete.…”

Section: Resultsmentioning

confidence: 89%

“…Despite the steady revolution of the wireless communication technology, the limited bandwidth remains as a main concern that would lead to data collisions when multiple data packets are transmitted simultaneously through a shared network [5], [22], [27], [39], [44]. A popular way of better utilizing the limited network resource is to adopt the communication protocols as so to administer the transmission order of network nodes, and some frequently deployed protocols include the Round-Robin (RR) protocol [17], [23], [24], [26], [37], [38], [46], the stochastic communication protocol [7], [15], [31], [45] and the Try-Once-Discard (TOD) protocol [30], [32].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recursive State Estimation for Stochastic Complex Networks Under Round-Robin Communication Protocol: Handling Packet Disorders

Liú

Wang

Liu

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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This paper investigates the recursive state estimation problem for a class of discrete-time stochastic complex networks with packet disorders under Round-Robin (RR) communication protocols. The phenomenon of packet disorders results from the random transmission delays during the signal propagation process due to the unpredictable fluctuations of the network load, and such random delays are modeled by a set of random variables satisfying certain known probability distributions. For the sake of lessening the communication burden and abating the data collisions, the RR protocol is introduced to govern the order of the nodes for data transmission. Under the scheduling of the RR protocol, only one node is allowed to gain the access to the network at each time instant. Then, a recursive estimator is devised to guarantee an upper bound for the estimation error covariance, and then the obtained upper bound is locally minimized by adequately choosing the estimator parameters. Furthermore, the boundedness of estimation error is analyzed in the sense of mean square with the help of stochastic analysis techniques. At last, a simulation example is presented to show the applicability of the proposed estimator design scheme.

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“…It is easy to see that the minimization of the upper bound (15) can be obtained when the estimator parameter is selected as (22), and the proof is complete.…”

Section: Resultsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Recursive State Estimation for Stochastic Complex Networks Under Round-Robin Communication Protocol: Handling Packet Disorders

Liú

Wang

Liu

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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“…In particular, the modified GAN has shown better performance than the conventional GAN. In our future work, we aim to: 1) develop an automatic hyperparameter selection scheme for GAN-based crack detection system based on evolutionary computation [31][32][33][34][35][36][37]; 2) apply our crack detection system to other thermography NDT tasks; 3) design a novel control strategy to enhance the detection performance of the crack detection system [38][39][40][41][42][43][44][45][46]; and 4) explore the dynamic behavior of the ECPT-based NDT process and study the thermography features based on signal processing and control theory [47][48][49][50][51][52][53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

et al. 2021

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As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.

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“…Experiment results have shown that the improved GAN outperforms the standard GAN for reconstructing the magnetic optical images, and the developed GAN-based crack detection method has demonstrated satisfactory performance in detecting the cracks in magnetic optical images. In the future, we aim to (1) employ our proposed GANbased crack detection for other NDT methods such as thermal imagery detection, ultrasound detection and X-ray imaging detection; (2) deploy evolutionary computation methods to optimize the parameter selection in the GAN-based crack detection system [24][25][26][27]; (3) employ signal processing and state estimation methods to investigate the stability of the NDT process [17,[19][20][21]28,34,35,42,46,54]; and (4) design an advanced control strategy to improve the detection accuracy and detection rate of the GAN-based crack detection system [14,18,33,47,50,52,55,56].…”

Section: Discussionmentioning

confidence: 99%

An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing

Tian

Wang

Liu

et al. 2021

Complex Intell. Syst.

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In this paper, an improved generative adversarial network (GAN) is proposed for the crack detection problem in electromagnetic nondestructive testing (NDT). To enhance the contrast ratio of the generated image, two additional regulation terms are introduced in the loss function of the underlying GAN. By applying an appropriate threshold to the segmentation of the generated image, the real crack areas and the fake crack areas (which are affected by the noises) are accurately distinguished. Experiments are carried out to show the superiority of the improved GAN over the original one on crack detection tasks, where a real-world NDT dataset is exploited that consists of magnetic optical images obtained using the electromagnetic NDT technique.

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Distributed non-fragile set-membership filtering for nonlinear systems under fading channels and bias injection attacks

Cited by 161 publications

References 44 publications

Recursive State Estimation for Stochastic Complex Networks Under Round-Robin Communication Protocol: Handling Packet Disorders

Recursive State Estimation for Stochastic Complex Networks Under Round-Robin Communication Protocol: Handling Packet Disorders

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing

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