Electro-hydrodynamic synchronization of piezoelectric flags

Hydrodynamic coupling of flexible flags in axial flows may profoundly influence their flapping dynamics, in particular driving their synchronization. This work investigates the effect of such coupling on the harvesting efficiency of coupled piezoelectric flags, that convert their periodic deformation into an electrical current. Considering two flags connected to a single output circuit, we investigate using numerical simulations the relative importance of hydrodynamic coupling to electrodynamic coupling of the flags through the output circuit due to the inverse piezoelectric effect. It is shown that electrodynamic coupling is dominant beyond a critical distance, and induces a synchronization of the flags' motion resulting in enhanced energy harvesting performance. We further show that this electrodynamic coupling can be strengthened using resonant harvesting circuits.Comment: 14 pages, 10 figures, to appear in J. Fluids Struc

Fluid-solid-electric lock-in of energy-harvesting piezoelectric flags

The spontaneous flapping of a flag in a steady flow can be used to power an output circuit using piezoelectric elements positioned at its surface. Here, we study numerically the effect of inductive circuits on the dynamics of this fluid-solid-electric system and on its energy harvesting efficiency. In particular, a destabilization of the system is identified leading to energy harvesting at lower flow velocities. Also, a frequency lock-in between the flag and the circuit is shown to significantly enhance the system's harvesting efficiency. These results suggest promising efficiency enhancements of such flow energy harvesters through the output circuit optimization.Comment: 8 pages, 8 figures, to appear in Physical Review Applie

Inductive effects on energy harvesting piezoelectric flag

National audienceInteraction between a flexible flag and a flow leads to a canonical fluid–structure instability whichproduces self-sustained vibrations, from which mechanical energy could be converted to electrical energythrough piezoelectric materials covering the flag and thus being deformed by its motion. We study thepossibility of harvesting this energy, especially the effect of an inductive circuit on the energy harvestingprocess. A destabilization of the coupled system is observed after adding an inductance. In the nonlinearcase, the harvesting efficiency increases significantly at lock–in between the frequencies of the flutteringflag and the electrical circuit.L'interaction d'un drapeau flexible avec un écoulement est connue pour donner lieu à une vibration auto-entretenue, dont l’énergie mécanique peut être convertie en énergie électrique par le biais des matériaux piézoélectriques qui couvrent le drapeau et ainsi se déforment avec celui-ci. On étudie la possibilité de récupérer cette énergie, et en particulier l'effet d'un circuit inductif sur le processus de récupération. Dans l’étude linéaire, une déstabilisation du système est observée par l'ajout d'une inductance. Une méthode numérique, basée sur une description explicite entre le couplage fluide–solide–électrique, est utilisée pour la simulation non-linéaire du système. En régime non-linéaire, l'efficacité de récupération augmente significativement lors de l'accrochage entre les fréquences de battement du drapeau et du circuit électrique

Modulation of Brain Tissue Transport and Endothelial Glycocalyx and Tight Junctions of the Blood-Brain Barrier by Transcranial Direct Current Stimulation

Transcranial direct current stimulation (tDCS) is a non-invasive approach to treat a broad range of brain disorders and to enhance memory and cognition in healthy individuals. In addition to directly acting on neurons by modulating the membrane potential, inducing neuronal polarization and changing cortical excitability in the brain to achieve its therapeutic effects, prior studies found that tDCS can transiently enhance the permeability (P) of the blood-brain barrier (BBB), the interface between blood circulation and brain tissue. Brain extracellular space (ECS) is a narrow microenvironment which surrounds every cell in the central nervous system (CNS). ECS occupies ~20% of brain tissue and contains interstitial fluid with ions and negatively charged extracellular matrix (ECM). The first part of the dissertation aimed to show that tDCS can also modulate ECS by transiently increasing solute brain tissue diffusion coefficients (Deff). In vivo multiphoton microscopy was used to quantify Deff in rat brain 5-30 min post tDCS. A mathematical model was applied to further predict the effect of tDCS on the ECS width and ECM density. By combining a transport model for the BBB with the in vivo data, a recent study predicted that one mechanism of tDCS enhancing the BBB permeability is to temporarily disrupt the endothelial glycocalyx (EG) and tight junctions of the BBB. The second part of the dissertation aimed to confirm this prediction indirectly in vivo by quantifying the BBB permeability to solutes with the same size but carrying opposite charges under control and after tDCS treatment. Since only the EG and the ECM in the BM of the BBB carry negative charges, if they are disrupted by tDCS, the BBB permeability should become identical for the solutes with the same size but opposite charges. Due to the transient and nano-meter scale changes in the BBB by tDCS, it is challenging to measure the alteration of EG and tight junctions in vivo. Instead, the third part of the dissertation quantified the EG and tight junctions by using in vitro BBB models formed by brain microvascular endothelial cell monolayers and investigated the cellular mechanism by which DCS modulates these structural components

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms

Tensor regression based on linked multiway parameter analysis

Classical regression methods take vectors as covariates and estimate the corresponding vectors of regression parameters. When addressing regression problems on covariates of more complex form such as multi-dimensional arrays (i.e. tensors), traditional computational models can be severely compromised by ultrahigh dimensionality as well as complex structure. By exploiting the special structure of tensor covariates, the tensor regression model provides a promising solution to reduce the model’s dimensionality to a manageable level, thus leading to efficient estimation. Most of the existing tensor-based methods independently estimate each individual regression problem based on tensor decomposition which allows the simultaneous projections of an input tensor to more than one direction along each mode. As a matter of fact, multi-dimensional data are collected under the same or very similar conditions, so that data share some common latent components but can also have their own independent parameters for each regression task. Therefore, it is beneficial to analyse regression parameters among all the regressions in a linked way. In this paper, we propose a tensor regression model based on Tucker Decomposition, which identifies not only the common components of parameters across all the regression tasks, but also independent factors contributing to each particular regression task simultaneously. Under this paradigm, the number of independent parameters along each mode is constrained by a sparsity-preserving regulariser. Linked multiway parameter analysis and sparsity modeling further reduce the total number of parameters, with lower memory cost than their tensor-based counterparts. The effectiveness of the new method is demonstrated on real data sets

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers. Machine learning-based (ML-based) Android malware detection (AMD) methods are crucial in addressing this problem; however, their vulnerability to adversarial examples raises concerns. Current attacks against ML-based AMD methods demonstrate remarkable performance but rely on strong assumptions that may not be realistic in real-world scenarios, e.g., the knowledge requirements about feature space, model parameters, and training dataset. To address this limitation, we introduce AdvDroidZero, an efficient query-based attack framework against ML-based AMD methods that operates under the zero knowledge setting. Our extensive evaluation shows that AdvDroidZero is effective against various mainstream ML-based AMD methods, in particular, state-of-the-art such methods and real-world antivirus solutions.Comment: To Appear in the ACM Conference on Computer and Communications Security, November, 202