DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data

Multivariate time series(MTS) is a universal data type related to many practical applications. However, MTS suffers from missing data problems, which leads to degradation or even collapse of the downstream tasks, such as prediction and classification. The concurrent missing data handling procedures could inevitably arouse the biased estimation and redundancy-training problem when encountering multiple downstream tasks. This paper presents a universally applicable MTS pre-train model, DBT-DMAE, to conquer the abovementioned obstacle. First, a missing representation module is designed by introducing dynamic positional embedding and random masking processing to characterize the missing symptom. Second, we proposed an auto-encoder structure to obtain the generalized MTS encoded representation utilizing an ameliorated TCN structure called dynamic-bidirectional-TCN as the basic unit, which integrates the dynamic kernel and time-fliping trick to draw temporal features effectively. Finally, the overall feed-in and loss strategy is established to ensure the adequate training of the whole model. Comparative experiment results manifest that the DBT-DMAE outperforms the other state-of-the-art methods in six real-world datasets and two different downstream tasks. Moreover, ablation and interpretability experiments are delivered to verify the validity of DBT-DMAE's substructures

Dynamic characteristic analysis of two-stage quasi-zero stiffness vibration isolation system

A novel two-stage quasi-zero stiffness (QZS) vibration isolator was proposed for the purpose of low-frequency vibration isolation. Firstly, the dynamic model of the vibration isolation system was established; furthermore, the force transmissibility of the system under harmonic force excitation was derived by the averaging method; finally, the effects on the vibration isolation performance caused by excitation amplitude, mass ratio and damping ratio were discussed. Results show that, compared with the corresponding two-stage linear system, two-stage QZS system not only has better isolation performance, but also possesses a wider range of isolation frequency provided that the excitation amplitude, mass ratio and damping ratio is appropriate

Optimization of unequal-active-and-passive-area piezoelectric unimorph cantilevers with collisions for ultra-thin keyboard design

The purpose of this study is to optimize the design of piezoelectric unimorph cantilevers, for ultra-thin keyboard design, so that the first resonant frequency is located in the sensitive frequency range and the first resonant amplitude is above the perception threshold of human hands for vibratory stimulus. The piezoelectric unimorphs used in this study have unequal active and passive areas. Simulations and experiments were first compared to find the effects of the dimensions on the first resonant frequency and displacement frequency response without collisions. A finite element model with collisions based on the verified boundary conditions was then built. Both the experiment data and simulation data was combined to build a regression model to predict the first resonant frequency with collisions for ultra-thin keyboard design. This study can help designers quickly design a vibrotactile device, in the early design stage

Robust Affinity Propagation using Preference Estimation

Affinity propagation is a novel unsupervised learning algorithm for exemplar-based clustering without the priori knowledge of the number of clusters (NC). In this article, the influence of the “preference” on the accuracy of AP output is addressed. We present a robust AP clustering method, which estimates what preference value could possibly yield an optimal clustering result. To demonstrate the performance promotion, we apply the robust AP on picture clustering, using local SIFT, global MPEG-7 CLD, and the proposed preference as the input of AP. The experimental results show that over 40% enhancement of ARI accuracy for several image datasets

Power of linkage analysis using traits generated from simulated longitudinal data of the Framingham Heart Study

The Framingham Heart Study is a very successful longitudinal research for cardiovascular diseases. The completion of a 10-cM genome scan in Framingham families provided an opportunity to evaluate linkage using longitudinal data. Several descriptive traits based on simulated longitudinal data from the Genetic Analysis Workshop 13 (GAW13) were generated, and linkage analyses were performed for these traits. We compared the power of detecting linkage for baseline and slope genes in the simulated data of GAW13 using these traits. We found that using longitudinal traits based on multiple follow-ups may not be more powerful than using cross-sectional traits for genetic linkage analysis