Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks

Kalman filters and observers are two main classes of dynamic state estimation (DSE) routines. Power system DSE has been implemented by various Kalman filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we discuss two challenges for an effective power system DSE: (a) model uncertainty and (b) potential cyber attacks. To address this, the cubature Kalman filter (CKF) and a nonlinear observer are introduced and implemented. Various Kalman filters and the observer are then tested on the 16-machine, 68-bus system given realistic scenarios under model uncertainty and different types of cyber attacks against synchrophasor measurements. It is shown that CKF and the observer are more robust to model uncertainty and cyber attacks than their counterparts. Based on the tests, a thorough qualitative comparison is also performed for Kalman filter routines and observers.Comment: arXiv admin note: text overlap with arXiv:1508.0725

Displacement mechanism of polymeric surfactant in chemical cold flooding for heavy oil based on microscopic visualization experiments

 In order to study the microscopic oil displacement mechanism of polymeric surfactant in chemical cold flooding for heavy oil, the indoor microscopic visualization displacement experiments were carried out. The flooding experiment of heavy oil was conducted by using water, osmotic modified oil displacing agent (a kind of polymeric surfactant) and water-in-oil emulsion (obtained by mixing polymeric surfactant and heavy oil) as displacing phases to study the mechanism of polymeric surfactant to enhance oil recovery in heavy oil reservoir. The experimental results show that the polymeric surfactant can increase the viscosity of the water phase, reduce the water-oil mobility ratio, expand the swept area, and there is no obvious fingering phenomenon which occurs during water flooding. The polymeric surfactant has the surfactant characteristics which can reduce the interfacial tension between oil and water to promote the formation of oil droplets with smaller droplet diameter. And the interfacial film composed of polymeric surfactant molecules will be formed on the surface of oil droplets to prevent the coalescence of oil droplets and improve the flow ability of oil phase. The water-in-oil emulsion can be miscible with the oil in heavy oil displacement process, and thus sweeps the areas such as the dead pores which cannot be swept by water and polymeric surfactant flooding, which increases the sweep efficiency to a certain extent.Cited as: Xu, F., Chen, Q., Ma, M., Wang, Y., Yu, F., Li, J. Displacement mechanism of polymeric surfactant in chemical cold flooding for heavy oil based on microscopic visualization experiments. Advances in Geo-Energy Research, 2020, 4(1): 77-85, doi: 10.26804/ager.2020.01.0

A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives

Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs embedded with well-designed triggers while behaving normally on clean inputs. Many works have explored the invisibility of backdoor triggers to improve attack stealthiness. However, most of them only consider the invisibility in the spatial domain without explicitly accounting for the generation of invisible triggers in the frequency domain, making the generated poisoned images be easily detected by recent defense methods. To address this issue, in this paper, we propose a DUal stealthy BAckdoor attack method named DUBA, which simultaneously considers the invisibility of triggers in both the spatial and frequency domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we first use Discrete Wavelet Transform to embed the high-frequency information of the trigger image into the clean image to ensure attack effectiveness. Then, to attain strong stealthiness, we incorporate Fourier Transform and Discrete Cosine Transform to mix the poisoned image and clean image in the frequency domain. Moreover, the proposed DUBA adopts a novel attack strategy, in which the model is trained with weak triggers and attacked with strong triggers to further enhance the attack performance and stealthiness. We extensively evaluate DUBA against popular image classifiers on four datasets. The results demonstrate that it significantly outperforms the state-of-the-art backdoor attacks in terms of the attack success rate and stealthinessComment: 10 pages, 7 figures. Submit to ACM MM 202