Deng Wenfeng

• PhD Student at Central South University

Deep neural networks (DNNs) as one of the key enabling technologies have been widely used in Industrial Artificial Intelligence (IAI). However, recent research has revealed that they are quite vulnerable to adversarial attacks, arousing serious concerns about DNNs' robustness in many IAI-driven applications, such as industrial video analysis tasks....

The massive integration of advanced cyber technologies with power grids has expanded the attack surface area of cyber-physical power systems (CPPSs), where timely detection is of paramount importance for their safe and reliable operation. However, most studies on securing CPPS rarely considered resource constraints, resulting in the unsatisfactory...

Due to the complicated production mechanism in multivariate industrial processes, different dynamic features of variables raise challenges to traditional data-driven process monitoring methods which assume the process data is static or dynamically consistent. To tackle this issue, this paper proposes a novel process monitoring method based on the l...

Reconstructing the interacting topology from measurable data is fundamental to understanding, controlling, and predicting the collective dynamics of complex networked systems. Many methods have been proposed to address the basic inverse problem and have achieved satisfactory performance. However, a significant challenge arises when we attempt to de...

The networked systems are booming in multi-disciplines, including the industrial engineering system, the social system, and so on. The network structure is a prerequisite for the understanding and exploration of networked systems. However, the network structure is always unknown in practice, thus, it is significant yet challenging to investigate th...

Smart grid has become the trend of future power system owing to its efficient allocation of resources, real-time monitoring and decision-making ability. However, due to the deep integration of power physical system and information system, smart grid is under serious threat such as malicious attacks and so on. The false data injection attack (FDIA),...

Identification of complex networks from limited and noise contaminated data is an important yet challenging task, which has attracted researchers from different disciplines recently. In this paper, the underlying feature of a complex network identification problem was analyzed and translated into a sparse linear programming problem. Then, a general...

As one of the most effective technologies for network reconstruction, compressive sensing can recover signals from a small amount of observed data through sparse search or greedy algorithms in the assumption that the unknown signal is sufficiently sparse on a specific basis. However, there often occurs loss of precision even failure in the process...

Smart grid (SG) can automatically collect a large amount of data of different power parameters through different sensors, which has become the future trend of power systems, especially for real-time monitoring needs. However, due to the limitation of detection technology and measurement cost, direct measurement of SG topology is difficult. Thus, re...

Network structure reconstruction is a fundamental problem for understanding, predicting and controlling the behaviors of complex networked systems and has received growing attention due to the potentials in a wide range of fields. Recent years have witnessed dramatic advances in the field of network structure reconstruction, especially the famous c...

Complex network has proven to be a general model to characterize interactions of practical complex systems. Recently, reconstructing the structure of complex networks with limited and noisy data attracts much research attention and has gradually become a hotspot. However, the collected data are often contaminated by unknown outliers inevitably, whi...

Complex networks are widely used to describe the interactions of real systems such as technological, social and biological systems. Compressed sensing method is one of the most effective data-driven methods which has been used to reconstruct the underlying structure of network from small amounts of measurement data. Although the compressed sensing-...

How did cooperative strategy evolve remains an open question across disciplines. In most previous studies, they mainly consider the analyzing of game dynamics on the networked multiagent system under different mechanisms. However, there often exists a “government” who regulates the strategies of agents centralized or decentralized in reality. Motiv...