ISSN 0253-2778

CN 34-1054/N

2020 Vol. 50, No. 10

Display Method:
Research Article
Compressibility effect on shock-induced air/helium chevron interface evolution
GUO Xu, ZHAI Zhigang, LUO Xisheng
2020, 50(10): 1279-1290. doi: 10.3969/j.issn.0253-2778.2020.10.001
Shock tube experiments of a periodic air-helium chevron interface impacted by a planar shock wave are conducted. Effects of the compressibility and the initial amplitude on the perturbation growth are highlighted. For small initial amplitudes, the shock Mach number has limited effects on the reliability of the linear model. For high initial amplitudes, however, the linear model is generally invalid because the high amplitude effect will reduce the linear growth rate. Under the high initial amplitude condition, the increase of the shock Mach number further aggravates the discrepancy of the experimental result with the theoretical prediction. By considering the high amplitude effect and the high Mach number effect, the linear growth rate of the interface with high initial amplitude impacted by a strong shock wave can be well predicted. The compressibility effect induced by the incident shock wave can be illustrated by the material compression and the geometric compression of the interface, and the latter is found to be dominant. In the nonlinear regime, some nonlinear models proposed for single-mode interfaces are verified to be valid only at very early stages.
Sampling multivariate count variables with prespecified Pearson correlation using marginal regular vine copulas
YUAN Zhenfei, HU Taizhong
2020, 50(10): 1291-1302. doi: 10.3969/j.issn.0253-2778.2020.10.002
The problem of sampling multivariate count variables has practical significance. Ref.[1]proposed an algorithm for sampling multivariate count random variables based on C-vine copulas, by which the parameters
of edge
of the C-vine structure are estimated by optimizing the difference between the sample partial correlation
and the partial correlation
calculated from the prespecified correlation matrix by the Pearson recurrence formula, where
is a conditioning node set. We introduce the concept of marginal regular vine copula, which leads to directly optimizing the difference between the sample correlation
and the targeted correlation
for pairs of variables. Three simulation studies illustrate that the new sampling method generates more accurate results than the C-vine sampling method in Ref.[1]and the Naive sampling method in Ref.[3]. The sampling algorithm routines are implemented in Python as package countvar in PyPi.
Bayesian variable selection for proportional hazards model with current status data
CUI Di, ZHANG Weiping
2020, 50(10): 1303-1314. doi: 10.3969/j.issn.0253-2778.2020.10.003
A Bayesian proportional hazards (PH) model is proposed for analyzing current status data based on Expectation-Maximization Variable Selection (EMVS) method. This model can estimate parameters and select variables simultaneously, which efficiently improves model interpretability and predictive ability. To identify risk factors, appropriate priors are assigned on the indicator variables that denote the existence of covariates. The baseline cumulative hazard function is approximated via monotone splines. A novel Expectation-Maximization (EM) algorithm is developed for model fitting by using a two-stage data augmentation procedure involving latent Poisson variables. Finally, the performance of proposed method is investigated by simulations and a real data analysis.
Optimal time-locked trial strategy for software in the presence of piracy
YANG Feng, LANG Xiudong, ANG Sheng
2020, 50(10): 1315-1329. doi: 10.3969/j.issn.0253-2778.2020.10.004
Time-locked trial is one of the commonly used marketing strategies in the commercial software industry. After the trial, one problem facing software companies is possible pirates by users. A model is proposed to study how a monopolistic software supplier should respond to different pirating conditions by use of a time-locked trial strategy. The conditions are determined under which a trial strategy is optimal and was fund that if there is piracy, when the customer’s basic perception about the quality of the software is moderate, in the whole piracy region it is better for the company to offer trial while when the basic belief is relatively high or low, the company does not provide trial period unless the piracy cost is relatively tiny. It was also fund that the optimal trial period length decreases with the piracy cost when there is piracy, while the trend is reversed when there is only a threat of piracy. The optimal price increases with the cost of piracy when there is piracy or the threat of it. Versioning strategy and time-locked trial strategy were compared in the presence of piracy and it was fund that the latter is more applicable to enhancing profit. Reasons for these results and some managerial implications are given.
A literature review of enterprise green transformation from the perspective of complex social network and prospects
WAN Liang, FANG Wenpei, WANG Chengyuan, WANG Shanyong
2020, 50(10): 1330-1342. doi: 10.3969/j.issn.0253-2778.2020.10.005
How to promote the change of development mode through enterprise green transformation, to realize the balance between economic and social development and environmental quality is an urgent and important research topic. In this sense, the empirical test of the enterprise green transformation is not only among the key interests of academics, but also has strong policy implications. Different from the previous research on it, this paper attempts to start from the new research perspective of complex social network, uses the research history of enterprise green transformation as an entry point, systematically sorts out and analyzes the influencing factors, the mechanism and peer evolution that affect the enterprise green transformation. On the basis of this, an integrated theoretical framework is put forward, and some reference is provided for the development of relevant research on enterprise green transformation. This paper aims to broaden the research perspective of enterprise green transformation, enrich and improve enterprise green transformation theories and methods. The research results will help reveal the “black box” of green transformation, and provide government guidance for the diffusion of green transformation.
Adversarial attack based countermeasures against deep learning side-channel attacks
GU Ruizhe, WANG Ping, ZHENG Mengce, HU Honggang, YU Nenghai
2020, 50(10): 1343-1358. doi: 10.3969/j.issn.0253-2778.2020.10.006
Numerous previous works have studied deep learning algorithms applied in the context of side-channel attacks, which demonstrated the ability to perform successful key recoveries. These studies show that modern cryptographic devices are increasingly threatened by side-channel attacks with the help of deep learning. However, the existing countermeasures are designed to resist classical side-channel attacks, and cannot protect cryptographic devices from deep learning based side-channel attacks. Thus, there arises a strong need for countermeasures against deep learning based side-channel attacks. Although deep learning has the high potential in solving complex problems, it is vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to give wrong pedictions. In this paper, a kind of novel countermeasures is proposed based on adversarial attacks that is specifically designed against deep learning based side-channel attacks. We estimate several models commonly used in deep learning based side-channel attacks to evaluate the proposed countermeasures. It is shown that our approach can effectively protect cryptographic devices from deep learning based side-channel attacks in practice. In addition, our experiments show that the new countermeasures can also resist classical side-channel attacks.