Finite element model updating using base excitation response function

Finite element model updating is an effective way to build accurate analytical models for structures. Most of the available updating methods employ information from modal testing. However, in astronautics engineering, information provided by vibration table testing is more valuable than those from modal testing. Therefore, it is necessary to study updating methods which can adopt information from vibration table testing. This paper presents the study on such issue. The base excitation response function is analyzed with the assumption that the vibration table gives the structure a single direction motion excitation. Model updating method which adopts the response function is then proposed. In the numerical simulation, several case studies are constructed for a truss structure with small or significant modeling errors respectively. Data selection, which has great influence on the success of updating, is carefully studied. A novel adaptive data selection approach is suggested. Simulation results show that model updating converge with good accuracy when the adaptive data selection approach is used

Automatic Nonlinear Subspace Identification Using Clustering Judgment Based on Similarity Filtering

Accurately determining system order plays a vital role in system identification directly related to the accuracy of identification results, especially for nonlinear system identification. Due to the need for human subjective judgment, the traditional sequence determination method easily causes uncertainty in the results; and the phenomenon of the virtual mode or omission occurs. An automatic nonlinear subspace identification method is proposed to address the aforementioned problems. When the eigenvalue decomposition of the constructed Hankel matrix is performed, the calculation range of the modal order of the system is estimated. The similarity coefficient and distance function are introduced to cluster the identified modal results, the poles of the false modes are removed to obtain the cluster stabilization diagram, and the best order of the system is received. Then, the modal parameters and nonlinear coefficients are obtained. Simulation examples are carried out to verify the effectiveness and robustness of the proposed method. An experimental study is carried out on a multilayer building with nonlinear characteristics. Compared with the traditional stabilization graph, the accuracy of the automatic order determination proposed in this paper is proven

Federated Variational Learning for Anomaly Detection in Multivariate Time Series

Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic nature of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework in a federated fashion to continuously monitor the behaviors of interconnected devices within a network and alert for abnormal incidents so that countermeasures can be taken before undesired consequences occur. To be specific, we leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures feature and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models. We also conduct extensive experiments to demonstrate the effectiveness of our detection framework under non-federated and federated settings in terms of overall performance and detection latency

Finite element model updating using base excitation response function

Finite element model updating is an effective way to build accurate analytical models for structures. Most of the available updating methods employ information from modal testing. However, in astronautics engineering, information provided by vibration table testing is more valuable than those from modal testing. Therefore, it is necessary to study updating methods which can adopt information from vibration table testing. This paper presents the study on such issue. The base excitation response function is analyzed with the assumption that the vibration table gives the structure a single direction motion excitation. Model updating method which adopts the response function is then proposed. In the numerical simulation, several case studies are constructed for a truss structure with small or significant modeling errors respectively. Data selection, which has great influence on the success of updating, is carefully studied. A novel adaptive data selection approach is suggested. Simulation results show that model updating converge with good accuracy when the adaptive data selection approach is used

Influenza Virus Database (IVDB): an integrated information resource and analysis platform for influenza virus research

Frequent outbreaks of highly pathogenic avian influenza and the increasing data available for comparative analysis require a central database specialized in influenza viruses (IVs). We have established the Influenza Virus Database (IVDB) to integrate information and create an analysis platform for genetic, genomic, and phylogenetic studies of the virus. IVDB hosts complete genome sequences of influenza A virus generated by Beijing Institute of Genomics (BIG) and curates all other published IV sequences after expert annotation. Our Q-Filter system classifies and ranks all nucleotide sequences into seven categories according to sequence content and integrity. IVDB provides a series of tools and viewers for comparative analysis of the viral genomes, genes, genetic polymorphisms and phylogenetic relationships. A search system has been developed for users to retrieve a combination of different data types by setting search options. To facilitate analysis of global viral transmission and evolution, the IV Sequence Distribution Tool (IVDT) has been developed to display the worldwide geographic distribution of chosen viral genotypes and to couple genomic data with epidemiological data. The BLAST, multiple sequence alignment and phylogenetic analysis tools were integrated for online data analysis. Furthermore, IVDB offers instant access to pre-computed alignments and polymorphisms of IV genes and proteins, and presents the results as SNP distribution plots and minor allele distributions. IVDB is publicly available a

Design and Demonstration of a TDD-Based Central-Coordinated Resource-Reserved Multiple Access (CRMA) Scheme for Bidirectional VLC Networking

The sharply growing demand for increased transmission capacity and bandwidth in last meter and last mile access networks together with the commercialization of fifth generation (5G) wireless systems has been opening up new opportunities for non-radio frequency (RF)-based wireless technologies. Visible light communications (VLC) technology is a potential candidate for access networking in 5G, which offers a higher spectral efficiency than RF-based Femtocell networks by three orders of magnitude. This paper proposes an all wireless optical bidirectional VLC multiple access scheme for pure VLC network access points in terminals. Centralized coordination is adopted to reduce the system complexity. And reservation of resource is introduced to guarantee collision avoidance during data frame transmission. The proposed multiple access scheme introduces special system parameters to achieve the balance of system throughput and access latency. The feasibility of the proposed scheme is verified by both theoretical analysis and experimental investigation. We show that the proposed scheme is suitable for a bidirectional pure VLC access network and can be used as a supplement in the IEEE 802.11 bb for 5G+bidirectional VLC application scenarios

Tuning microtubule dynamics to enhance cancer therapy by modulating FER-mediated CRMP2 phosphorylation

Though used widely in cancer therapy, paclitaxel only elicits a response in a fraction of patients. A strong determinant of paclitaxel tumor response is the state of microtubule dynamic instability. However, whether the manipulation of this physiological process can be controlled to enhance paclitaxel response has not been tested. Here, we show a previously unrecognized role of the microtubule-associated protein CRMP2 in inducing microtubule bundling through its carboxy terminus. This activity is significantly decreased when the FER tyrosine kinase phosphorylates CRMP2 at Y479 and Y499. The crystal structures of wild-type CRMP2 and CRMP2-Y479E reveal how mimicking phosphorylation prevents tetramerization of CRMP2. Depletion of FER or reducing its catalytic activity using sub-therapeutic doses of inhibitors increases paclitaxel-induced microtubule stability and cytotoxicity in ovarian cancer cells and in vivo. This work provides a rationale for inhibiting FER-mediated CRMP2 phosphorylation to enhance paclitaxel on-target activity for cancer therapy

A novel mucosal absorption enhancer: 2-guanidyl chitosan

Master'sMASTER OF SCIENCE (PHARMACY