Ensemble Kalman filtering for hydraulic conductivity characterization: Parallelization and non-Gaussianity

Tesis por compendioThe ensemble Kalman filter (EnKF) is nowadays recognized as an excellent inverse method for hydraulic conductivity characterization using transient piezometric head data. and it is proved that the EnKF is computationally efficient and capable of handling large fields compared to other inverse methods. However, it is needed a large ensemble size (Chen and Zhang, 2006) to get a high quality estimation, which means a lots of computation time. Parallel computing is an efficient alterative method to reduce the commutation time. Besides, although the EnKF is good accounting for the non linearities of the state equation, it fails when dealing with non-Gaussian distribution fields. Recently, many methods are developed trying to adapt the EnKF to non-Gaussian distributions(detailed in the History and present state chapter). Zhou et al. (2011, 2012) have proposed a Normal-Score Ensemble Kalman Filter (NS-EnKF) to character the non-Gaussian distributed conductivity fields, and already showed that transient piezometric head was enough for hydraulic conductivity characterization if a training image for the hydraulic conductivity was available. Then in this work, we will show that, when without such a training image but with enough transient piezometric head information, the performance of the updated ensemble of realizations in the characterization of the non-Gaussian reference field. In the end, we will introduce a new method for parameterizing geostatistical models coupling with the NS-EnKF in the characterization of a Heterogenous non-Gaussian hydraulic conductivity field. So, this doctor thesis is mainly including three parts, and the name of the parts as below. 1, Parallelized Ensemble Kalman Filter for Hydraulic Conductivity Characterization. 2, The Power of Transient Piezometric Head Data in Inverse Modeling: An Application of the Localized Normal-score EnKF with Covariance Inflation in a Heterogenous Bimodal Hydraulic Conductivity Field. 3, Parameterizing geostatistical models coupling with the NS-EnKF for Heterogenous Bimodal Hydraulic Conductivity characterization.Xu, T. (2014). Ensemble Kalman filtering for hydraulic conductivity characterization: Parallelization and non-Gaussianity [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/43769TESISCompendi

A Deep Learning based Detection Method for Combined Integrity-Availability Cyber Attacks in Power System

As one of the largest and most complex systems on earth, power grid (PG) operation and control have stepped forward as a compound analysis on both physical and cyber layers which makes it vulnerable to assaults from economic and security considerations. A new type of attack, namely as combined data Integrity-Availability attack, has been recently proposed, where the attackers can simultaneously manipulate and blind some measurements on SCADA system to mislead the control operation and keep stealthy. Compared with traditional FDIAs, this combined attack can further complicate and vitiate the model-based detection mechanism. To detect such attack, this paper proposes a novel random denoising LSTM-AE (LSTMRDAE) framework, where the spatial-temporal correlations of measurements can be explicitly captured and the unavailable data is countered by the random dropout layer. The proposed algorithm is evaluated and the performance is verified on a standard IEEE 118-bus system under various unseen attack attempts

Lightweight Digital Hardware Random Number Generators

Abstract — Random Number Generator (RNG) plays an essential role in many sensor network systems and applications, such as security and robust communication. We have developed the first digital hardware random number generator (DHRNG). DHRNG has a small footprint and requires ultra-low energy. It uses a new recursive structure that directly targets efficient FPGA implementation. The core idea is to place or extract random values in FPGA configuration bits and randomly connect the building blocks. We present our architecture, introduce accompanying protocols for secure public key communication, and adopt the NIST randomness test on the DHRNG’s output stream. I

Rheology and Shear-Induced Textures of Silver Nanowire Lyotropic Liquid Crystals

We report the rheological and microstructural shear response of a mixture of polyvinylpyrrolidone (PVP) coated silver nanowires and nanoparticle aggregates (Ag) dispersed in ethylene glycol (EG) or water (H2O). Biphasic and liquid crystalline dispersions exhibited rheological characteristics similar to those of lyotropic liquid crystalline polymers (LCPs). The viscosity versus concentration curve for Ag-EG dispersions showed a viscosity minimum at a silver concentration between 2.2 vol.% and 2.9 vol.%; this is indicative of the transition to an entirely liquid crystalline phase. The rheology results were consistent with optical microscopy observations that at 2.9 vol.% the sample was entirely birefringent; this is another indication of liquid crystalline phase formation. Shear had a significant effect on the microstructure of the dispersions and dried coatings. Depending on the shear rate, worm or shear banding Structures were observed

Contaminant Source Identification in Aquifers: A Critical View

[EN] Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers.The first author wishes to acknowledge the financial contribution of the Spanish Ministry of Science and Innovation through Project No. PID2019-109131RB-I00, and the second author acknowledges the financial support from the Fundamental Research Funds for the Central Universities (B200201015) and Jiangsu Specially-Appointed Professor Program from Jiangsu Provincial Department of Education (B19052). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Gómez-Hernández, JJ.; Xu, T. (2022). Contaminant Source Identification in Aquifers: A Critical View. Mathematical Geosciences. 54(2):437-458. https://doi.org/10.1007/s11004-021-09976-443745854