A practical piezoelectric vibration energy harvesting (PVEH) system is usually composed of two coupled parts: a harvesting structure and an interface circuit. Thus, it is much necessary to build system-level coupled models for analyzing PVEH systems, so that the whole PVEH system can be optimized to obtain a high overall efficiency. In this paper, two classes of coupled models are proposed by joint finite element and circuit analysis. The first one is to integrate the equivalent circuit model of the harvesting structure with the interface circuit and the second one is to integrate the equivalent electrical impedance of the interface circuit into the finite element model of the harvesting structure. Then equivalent circuit model parameters of the harvesting structure are estimated by finite element analysis and the equivalent electrical impedance of the interface circuit is derived by circuit analysis. In the end, simulations are done to validate and compare the proposed two classes of system-level coupled models. The results demonstrate that harvested powers from the two classes of coupled models approximate to theoretic values. Thus, the proposed coupled models can be used for system-level optimizations in engineering applications.
Credit card fraud detection (CCFD) is important for protecting the cardholder’s property and the reputation of banks. Class imbalance in credit card transaction data is a primary factor affecting the classification performance of current detection models. However, prior approaches are aimed at improving the prediction accuracy of the minority class samples (fraudulent transactions), but this usually leads to a significant drop in the model’s predictive performance for the majority class samples (legal transactions), which greatly increases the investigation cost for banks. In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. We validate the effectiveness of HELMDD on two real credit card datasets. The experimental results demonstrate that compared with current state-of-the-art models, HELMDD has the best comprehensive performance. HELMDD not only achieves good recall rates for both the minority class and the majority class but also increases the savings rate for banks to 0.8623 and 0.6696, respectively.
The task of person-level action recognition in complex events aims to densely detect pedestrians and individually predict their actions from surveillance videos. In this paper, we present a simple yet efficient pipeline for this task, referred to as TSD-TSM networks. Firstly, we adopt the TSD detector for the pedestrian localization on each single keyframe. Secondly, we generate the sequential ROIs for a person proposal by replicating the adjusted bounding box coordinates around the keyframe. Particularly, we propose to conduct straddling expansion and region squaring on the original bounding box of a person proposal to widen the potential space of motion and interaction and lead to a square box for ROI detection. Finally, we adapt the TSM classifier on the generated ROI sequences to perform action classification and further adopt late fusion to promote the prediction. Our proposed pipeline achieved the 3rd place in the ACM-MM 2020 grand challenge, i.e., Large-scale Human-centric Video Analysis in Complex Events (Track-4), obtaining final 15.31% wf-mAP@avg and 20.63% f-mAP@avg on the testing set. CCS CONCEPTS • Computing methodologies → Activity recognition and understanding.
Continuously powering wireless sensor nodes (WSNs) has been one key problem in structural health monitoring. Piezoelectric energy harvesting (PEH) from environmental vibrations has been a potential way to make low power consumption WSNs self-powered. One kind of vibration energy harvesting plate with local resonators embedded in piezoelectric patches is presented in this paper. Due to its distinct dynamic performances: band gaps, we can control wave propagating for the purpose of broad band vibration harvesting and higher energy conversion efficiency. Distributions and characteristics of band gaps are affected by geometric and material parameters, thus it's necessary to analyze the effects of these key parameters. In this paper, a theoretical calculation method of vibration propagation characteristics is developed based on finite element method (FEM) and the Floquet-Bloch theorem. Then finite element simulations using Comsol software are done to analyze the effects of different parameters. The results show that we can reduce the beginning frequency of the lowest band gap by increasing the length of resonators, while broadening band gaps by raising the filling ratio of the piezoelectric patches. On the other hand, Young modulus is the main factor of material parameters which markedly affects the beginning and cutoff frequency. The results provide useful theoretical guidelines for optimally designing vibration energy harvesting plates in applications.
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