ISSN 0253-2778

CN 34-1054/N

2021 Vol. 51, No. 1

Research Article
MOVIE: Mesh oriented video inpainting network
LIU Sen, ZHANG Zhizheng, YU Tao, CHEN Zhibo
2021, 51(1): 1-11. doi: 10.52396/JUST-2020-0022
Video inpainting aims to fill the holes across different frames upon limited spatio-temporal contexts. The existing schemes still suffer from achieving precise spatio-temporal coherence especially in hole areas due to inaccurate modeling of motion trajectories. In this paper, we introduce fexible shape-adaptive mesh as basic processing unit and mesh flow as motion representation, which has the capability of describing complex motions in hole areas more precisely and efficiently. We propose a Mesh Oriented Video Inpainting nEtwork, dubbed MOVIE, to estimate mesh flows then complete the hole region in the video. Specifically, we first design a mesh flow estimation module and a mesh flow completion module to estimate the mesh flow for visible contents and holes in a sequential way, which decouples the mesh flow estimation for visible and corrupted contents for easy optimization. A hybrid loss function is further introduced to optimize the flow estimation performance for the visible regions, the entire frames and the inpainted regions respectively. Then we design a polishing network to correct the distortion of the inpainted results caused by mesh flow transformation. Extensive experiments show that MOVIE not only achieves over four-times speed-up in completing the missing area, but also yields more promising results with much better inpainting quality in both quantitative and perceptual metrics.
Information Science
A cognitive diagnostic framework for computer science education based on probability graph model
Hu Xinying, He Yu, Sun Guangzhong
2021, 51(1): 12-21. doi: 10.52396/JUST-2020-0007
A new cognitive diagnostic framework was proposed to evaluate students' theoretical and practical abilities in computer science education. Based on the probability graph model, students' coding ability was introduced, then the students' theoretical and practical abilities was modeled. And a parallel optimization algorithm was proposed to train the model efficiently. Experimental results on multiple data sets show that the proposed model has a significant improvement in MAE and RMSE compared with the competing methods. The proposed model provides more accurate and comprehensive analysis results for computer science education.
Multi-path switching protection for networked control systems under unbounded DoS attacks
Zhu Qiaohui, Liang Qipeng, Kang Yu, Zhao Yunbo
2021, 51(1): 22-32. doi: 10.52396/JUST-2020-1137
The strategy design and closed-loop stability of networked control systems under unbounded denial of service (DoS) attacks are probed. A multi-path switching protection strategy is firstly designed by noticing the usually available multiple paths in data communication networks. The strategy consists of a DoS attack detection module at the actuator side to identify DoS attacks from normal data packet dropouts, and a multi-path switching module at the sensor side to effectively switch the data transmission path when necessary. Then, the sufficient conditions for the closed-loop system being global mean square asymptotic stability are given, with a corresponding controller gain design method. Numerical examples illustrate the effectiveness of the proposed approach.
Event-triggered sliding mode load frequency control for multi-area interconnected power systems under deception attacks
Liu Xinghua, Bai Dandan, Sun Baoren, Wen Jiayan, Lv Wenjun, Li Kun
2021, 51(1): 33-42. doi: 10.52396/JUST-2020-0033
In this paper, the problem of sliding mode load frequency control (LFC) is probed for the multi-area interconnected power system under deception attacks. In the case of deception attacks, a Luenberger observer is designed to generate state estimation of the multi-area power systems. An event-triggered mechanism is introduced to reduce the frequency of controller updates and communication between nodes. Sufficient conditions are proposed to achieve asymptotical stability by utilizing sliding mode control and Lyapunov-Krasovskii (L-K) functional method. Then the sliding mode controller is synthesized to ensure that the trajectory of the closed-loop system can be driven onto the prescribed sliding surface. Finally, the effectiveness of the design scheme is verified by a three-area interconnected power system.
Management Science and Engineering
Testing bubbles based on modified PWY method
YE Wuyi, LIU Weibo
2021, 51(1): 43-52. doi: 10.52396/JUST-2020-1138
In recent years, the identification and inspection of bubbles has appeared as an important research topic in the financial field. ADF testing method and the PWY alternative method are commonly used, with serial correlation in high-frequency financial time series. In order to remove the influence of serial correlation and be able to test the situation of multiple bubbles at the same sample period, we have made a correction to the PWY alternative method with serial correlation. The BSADF method is used to obtain the statistical sequence, and the corresponding modified critical value sequence is given based on the simulation one. According to the Shanghai Stock Index from 2000 to 2019, an empirical study was conducted based on the GSADF and the revised PWY method to identify and test the bubble phenomenon. The empirical results show that three bubbles appeared between 2000 and 2019, which is in line with the actual financial market conditions, and the traditional PWY method cannot detect all bubbles. Therefore, the GSADF and the modified PWY method given in this article can find bubbles in time and provide some guidance to identify market risks and prevent financial crises.
Effect of personal carbon trading on EV adoption behavior based on a stochastic Petri net
He Haonan, Ren Wei, Wang Zuohang, Zhao Chenyong, Wang Shanyong, Ma Fei
2021, 51(1): 53-64. doi: 10.52396/JUST-2020-0024
The increasing urgency of environmental issues and maturity of the upstream carbon trading schemes indicate that personal carbon trading (PCT) is likely to be implemented soon, which will significantly affect the green behavior of consumers. In this study, a stochastic Petri net (SPN) model was constructed to analyze the evolution of the residential EV adoption behavior under a PCT scheme and the impacts of the environmental awareness and the PCT scheme on the EV adoption behavior were quantified. The results of this work show that the introduction of PCT does not necessarily positively impact the EV adoption. An emission quota and “cap-and-trade” attributes can significantly increase the environmental awareness of consumers, which is a “double-edged sword” for the EV adoption behavior at this stage. Specifically, it raises questions about the actual low-carbon performance of EVs and changes in travel patterns while increasing the willingness of consumers to pay a premium for the low-carbon products. Therefore, the government should rationalize the strength of PCT policies and traditional incentives to maximize the goal of promoting the EV adoption. The results can aid in gaining a better understanding of the behavioral evolution of the consumer EV adoption under the PCT scheme and provide theoretical support for government policymaking and product design and pricing by EV companies.
Measure of riskiness based on RDEU model
Guo Chuanfeng, Du Xinze, Wu Qinyu, Mao Tiantian
2021, 51(1): 65-74. doi: 10.52396/JUST-2021-0012
Motivated by References[3,4], we introduce a new measure of riskiness based on the rank-dependent expected utility (RDEU) model. The new measure of riskiness is a generalized class of risk measures which includes the economic index of riskiness of Reference[3] and the operational measure of riskiness of Reference[4] as special cases. We probe into the basic properties as a measure of riskiness such as monotonicity, positive homogeneity and subadditivity. We study its applications in comparative risk aversion as well. In addition, we present a simulation to illustrate the results.
Engineering and Materials Science
Temperature predictions of a single-room fire based on the CoKriging model
Shen Di, Jiang Yong, Zhu Xianli, Li Mengjie
2021, 51(1): 75-86. doi: 10.52396/JUST-2020-1140
This paper aims at accurately predict the smoke temperature in a single-room fire. Since both high-fidelity simulations and single-fidelity surrogate models cost much computational time, it is hard to meet the emergency needs of fire safety management. Therefore, a multi-fidelity model named CoKriging was introduced , which made use of the simulation data from Consolidate Fire and Smoke Transport (CFAST) and Fire Dynamic Simulator (FDS) for training. The leave-one-out cross-validation suggests that this model has been effectively trained when the data ratio of CFAST to FDS is 10∶1. Further comparisons among different methods show that the prediction accuracy of CoKriging is comparable to that of artificial neural network (ANN) and Kriging, while the modeling time is only 1/10 of the latter. Additionally, the predicted temperatures of CoKriging are very close to the simulated results of FDS, and once the CoKriging model is successfully constructed, much less time will be taken to make a new prediction than that of FDS. The exploratory research on the proportion of high-and low-fidelity data to the prediction results of CoKriging shows that there is no obvious correlation between them, and the prediction accuracy can still be ensured even if only a small amount of FDS data participates in model testing. In conclusion, the CoKriging model could be used as a fast and effective regression analysis method for the temperature prediction in a single-room fire.