Biblio

With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts. Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method. Experimental results demonstrate the excellence of the proposed method in different testing conditions.

The CPS-featured modern asynchronous grids interconnected with HVDC tie-lines facing the hazards from bulk power imbalance shock. With the aid of cyber layer, the SCPIFS incorporates the frequency stability constrains is put forwarded. When there is bulk power imbalance caused by HVDC tie-lines block incident or unplanned loads increasing, the proposed SCPIFS ensures the safety and frequency stability of both grids at two terminals of the HVDC tie-line, also keeps the grids operate economically. To keep frequency stability, the controllable variables in security control strategy include loads, generators outputs and the power transferred in HVDC tie-lines. McCormick envelope method and ADMM are introduced to solve the proposed SCPIFS optimization model. Case studies of two-area benchmark system verify the safety and economical benefits of the SCPFS. HVDC tie-line transferred power can take the advantage of low cost generator resource of both sides utmost and avoid the load shedding via tuning the power transferred through the operating tie-lines, thus the operation of both connected asynchronous grids is within the limit of frequency stability domain.

On cloud computing systems consisting of task queuing and resource allocations, it is essential but hard to model and evaluate the global performance. In most of the models, researchers use a stochastic process or several stochastic processes to describe a real system. However, due to the absence of theoretical conclusions of any arbitrary stochastic processes, they approximate the complicated model into simple processes that have mathematical results, such as Markov processes. Our purpose is to give a universal method to deal with common stochastic processes as long as the processes can be expressed in the form of transition matrix. To achieve our purpose, we firstly prove several theorems about the convergence of stochastic matrices to figure out what kind of matrix-defined systems has steady states. Furthermore, we propose two strategies for measuring the rate of convergence which reflects how fast the system would come to its steady state. Finally, we give a method for reducing a stochastic matrix into smaller ones, and perform some experiments to illustrate our strategies in practice.