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C
Zhao, H., Ren, J., Pei, Z., Cai, Z., Dai, Q., Wei, W..  2017.  Compressive Sensing Based Feature Residual for Image Steganalysis Detection. 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). :1096–1100.

Based on the feature analysis of image content, this paper proposes a novel steganalytic method for grayscale images in spatial domain. In this work, we firstly investigates directional lifting wavelet transform (DLWT) as a sparse representation in compressive sensing (CS) domain. Then a block CS (BCS) measurement matrix is designed by using the generalized Gaussian distribution (GGD) model, in which the measurement matrix can be used to sense the DLWT coefficients of images to reflect the feature residual introduced by steganography. Extensive experiments are showed that proposed scheme CS-based is feasible and universal for detecting stegography in spatial domain.

L
Yongdong, C., Wei, W., Yanling, Z., Jinshuai, W..  2018.  Lightweight Security Signaling Mechanism in Optical Network for Smart Power Grid. 2018 IEEE International Conference on Computer and Communication Engineering Technology (CCET). :110–113.

The communication security issue brought by Smart Grid is of great importance and should not be ignored in backbone optical networks. With the aim to solve this problem, this paper firstly conducts deep analysis into the security challenge of optical network under smart power grid environment and proposes a so-called lightweight security signaling mechanism of multi-domain optical network for Energy Internet. The proposed scheme makes full advantage of current signaling protocol with some necessary extensions and security improvement. Thus, this lightweight security signaling protocol is designed to make sure the end-to-end trusted connection. Under the multi-domain communication services of smart power grid, evaluation simulation for the signaling interaction is conducted. Simulation results show that this proposed approach can greatly improve the security level of large-scale multi-domain optical network for smart power grid with better performance in term of connection success rate performance.

S
Hongbin, Z., Wei, W., Wengdong, S..  2020.  Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence. 2020 IEEE/IAS Industrial and Commercial Power System Asia (I CPS Asia). :1474—1479.
The transmission line tower is affected by the surface subsidence in the mined out area of coal mine, which will appear the phenomenon of subsidence, inclination and even tower collapse, threatening the operation safety of the transmission line tower in the mined out area. Therefore, a Safety and Damage Assessment Method of Transmission Line Tower in Goaf Based on Artificial Intelligence is proposed. Firstly, the geometric model of the coal seam in the goaf and the structural reliability model of the transmission line tower are constructed to evaluate the safety. Then, the random forest algorithm in artificial intelligence is used to evaluate the damage of the tower, so as to take protective measures in time. Finally, a finite element simulation model of tower foundation interaction is built, and its safety (force) and damage identification are experimentally analyzed. The results show that the proposed method can ensure high accuracy of damage assessment and reliable judgment of transmission line tower safety within the allowable error.