Magnetorheological Elastomers: Materials and Applications

Magnetorheological elastomers (MREs) are a type of soft magneto-active rubber-like material, whose physical or mechanical properties can be altered upon the application of a magnetic field. In general, MREs can be prepared by mixing micron-sized magnetic particles into nonmagnetic rubber-like matrices. In this chapter, the materials, the preparing methods, the analytical models, and the applications of MREs are reviewed. First, different kinds of magnetic particles and rubber-like matrices used to prepare MREs, as well as the preparing methods, will be introduced. Second, some examples of the microstructures, as well as the microstructure-based analytical models, of MREs will be shown. Moreover, the magnetic field-induced changes of the macroscopic physical or mechanical properties of MREs will be experimentally given. Third, the applications of MREs in engineering fields will be introduced and the promising applications of MREs will be forecasted. This chapter aims to bring the reader a first-meeting introduction for quickly knowing about MREs, instead of a very deep understanding of MREs

Geophysical Monitoring of CO2 Injection at Citronelle Field, Alabama

Carbon dioxide (CO2) injection at the Citronelle oil field in Alabama has been deployed to determine the feasibility of carbon storage and enhanced oil recovery (EOR) in the depleted oil field. Citronelle is a small size city right above the oil field, hence, to detect geohazard risks, geophysical testing method using wireless sensor, and passive seismic technique is used: the non-intrusive measurements were made at well sites along two linear arrays. The outcomes of the geophysical monitoring at the Citronelle oil field are shear-wave velocity profiles that are correlated to the static stress distribution at different injection stages. Injection history interpretation using the stress wave monitoring indicates that CO2 injection resulted in the stressing of the strata

Efficient oblivious transfer with membership verification

tru

Magneto-Sensitive Smart Materials and Magnetorheological Mechanism

Magneto-sensitive smart materials, also named as magnetorheological (MR) materials, are a class of smart composites prepared by dispersing nanometer- or micrometer-sized ferromagnetic fillers into the different carrier matrix. As the rheological properties can be controlled by an external magnetic field rapidly, reversibly, and continuously, magneto-sensitive smart materials have great application potential in construction, automotive industry, artificial intelligence, etc. In this chapter, a brief history and classification of magneto-sensitive smart materials are firstly summarized. Next, we discuss the state-of-the-art of the magnetorheological mechanism through experimental and theoretical studies, respectively. Finally, the prospect for this material in the future is presented

Strong identity-based proxy signature schemes, revisited

tru

Energy Consumption Model of WSN Based on Manifold Learning Algorithm

Energy saving is one of the most important issues in wireless sensor networks. In order to effectively model the energy consumption -in wireless sensor network, a novel model is proposed based on manifold learning algorithm. Firstly, the components of the energy consumption by computational equations are measured, and the objective function is optimized. Secondly, the parameters in computational equations are estimated by manifold learning algorithm. Finally, the simulation experiments on OPNET and MATLAB Simulink are performed to evaluate the key factors influencing the model. The experimental results show that the proposed model had significant advantage in terms of synchronization accuracy and residual energy in comparison with other methods

Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization

Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization. In this paper, we propose a novel BO algorithm called Neighbor Regularized Bayesian Optimization (NRBO) to solve the problem. We first propose a neighbor-based regularization to smooth each sample observation, which could reduce the observation noise efficiently without any extra training cost. Since the neighbor regularization highly depends on the sample density of a neighbor area, we further design a density-based acquisition function to adjust the acquisition reward and obtain more stable statistics. In addition, we design a adjustment mechanism to ensure the framework maintains a reasonable regularization strength and density reward conditioned on remaining computation resources. We conduct experiments on the bayesmark benchmark and important computer vision benchmarks such as ImageNet and COCO. Extensive experiments demonstrate the effectiveness of NRBO and it consistently outperforms other state-of-the-art methods.Comment: Accepted by BMVC 202

STUDY OF CO2 INJECTION AT CITRONELLE OIL FIELD USING LUMPED MASS MODELING AND FIELD DATA VALIDATION

Carbon sequestration in geological formation is an ongoing effort of the research community to address the issue of curbing excessive anthropogenic CO2 emissions. This dissertation focuses on the development of a theoretical framework in establishing the criteria for geophysical monitoring using passive seismic method. The theoretical framework is established via modeling geological formation using a multi-degree of freedom model. Three main aspects are introduced in this dissertation: 1) the DoReMi passive sensing technique used to monitor CO2 injection at Citronelle Oil Field in Alabama; 2) the multi-physical MDOF lumped mass model employed to simulate wave propagation in an oil field in both linear and nonlinear conditions; and 3) comparisons of the shear wave velocity obtained from the experimental data and numerical simulation results. Field test results show that shear wave velocity of the strata in the reservoir and the stress changes are consistent. Stress change at oil bearing layer and calcite strata in inverse relationship. The proposed MDOF model accounts for the influence of stiffness of the geomaterial, which include oil-bearing layer and calcite and saline sand stones. The geological formation of Citronelle Oil Field is used in the numerical simulation. A fourth order Runge-Kutta method is employed for the time integration and a Matlab program was developed for this study. The wave response from the MDOF lumped mass model are changing with the changing properties of CO2 storage layer and overburden layers (calcite and saline sand layer). In linear condition, as the stiffness of oil bearing layer changes, spectral amplitude percentage difference (SAPD) value change is frequency dependent and higher frequency experienced larger changes than lower frequency amplitudes. ???/?? (velocity changes) derived from the simulation results show that the changes varied with depth are detectable. As the stiffness of calcite and saline layer increases, ???/?? has significant changes on the magnitude (as large as 0.35) and similar to the ???/?? obtained from experimental results. In nonlinear condition, as the stiffness of oil bearing layer changes is controlled by positive or absolute displacement, simulation results show some frequency modes are more sensitive than other frequencies. However, nonlinear phenomenon is not fully understood and need further investigation. Tripartite spectral plots (TSP) show good visual differences for site condition changes in both linear and nonlinear conditions, but are too complicated to interpret. The study of the research provided a theoretical understanding of the wave phenomena involved in a typical oil field that is undergoing CO2-EOR process, and the modeling technique can be used to guide the design of geophysical monitoring scheme in the oil field with different geological conditions

PriBioAuth: Privacy-preserving biometric-based remote user authentication

National Research Foundation (NRF) Singapor

A new framework for privacy-preserving biometric-based remote user authentication

tru