Engineering Commons^™

Material State Awareness For Composites Part Ii: Precursor Damage Analysis And Quantification Of Degraded Material Properties Using Quantitative Ultrasonic Image Correlation (Quic), Subir Patra, Sourav Banerjee

Faculty Publications

Material state awareness of composites using conventional Nondestructive Evaluation (NDE) method is limited by finding the size and the locations of the cracks and the delamination in a composite structure. To aid the progressive failure models using the slow growth criteria, the awareness of the precursor damage state and quantification of the degraded material properties is necessary, which is challenging using the current NDE methods. To quantify the material state, a new offline NDE method is reported herein. The new method named Quantitative Ultrasonic Image Correlation (QUIC) is devised, where the concept of microcontinuum mechanics is hybrid with the experimentally …

Material State Awareness For Composites Part I: Precursor Damage Analysis Using Ultrasonic Guided Coda Wave Interferometry (Cwi), Subir Patra, Sourav Banerjee

Faculty Publications

Detection of precursor damage followed by the quantification of the degraded material properties could lead to more accurate progressive failure models for composite materials. However, such information is not readily available. In composite materials, the precursor damages—for example matrix cracking, microcracks, voids, interlaminar pre-delamination crack joining matrix cracks, fiber micro-buckling, local fiber breakage, local debonding, etc.—are insensitive to the low-frequency ultrasonic guided-wave-based online nondestructive evaluation (NDE) or Structural Health Monitoring (SHM) (~100–~500 kHz) systems. Overcoming this barrier, in this article, an online ultrasonic technique is proposed using the coda part of the guided wave signal, which is often neglected. Although …

Reconstructing Yeasts Phylogenies And Ancestors From Whole Genome Data, Bing Feng, Yu Lin, Lingxi Zhou, Yan Guo, Robert Friedman, Roufan Xia, Chao Liu, Jijun Tang

Faculty Publications

Phylogenetic studies aim to discover evolutionary relationships and histories. These studies are based on similarities of morphological characters and molecular sequences. Currently, widely accepted phylogenetic approaches are based on multiple sequence alignments, which analyze shared gene datasets and concatenate/coalesce these results to a final phylogeny with maximum support. However, these approaches still have limitations, and often have conflicting results with each other. Reconstructing ancestral genomes helps us understand mechanisms and corresponding consequences of evolution. Most existing genome level phylogeny and ancestor reconstruction methods can only process simplified real genome datasets or simulated datasets with identical genome content, unique genome markers, …

Reconstructing Yeasts Phylogenies And Ancestors From Whole Genome Data, Bing Feng, Yu Ling, Lingxi Zhou, Roufan Xia, Fei Hu, Chao Liu

Faculty Publications

Phylogenetic studies aim to discover evolutionary relationships and histories. These studies are based on similarities of morphological characters and molecular sequences. Currently, widely accepted phylogenetic approaches are based on multiple sequence alignments, which analyze shared gene datasets and concatenate/coalesce these results to a final phylogeny with maximum support. However, these approaches still have limitations, and often have conflicting results with each other. Reconstructing ancestral genomes helps us understand mechanisms and corresponding consequences of evolution. Most existing genome level phylogeny and ancestor reconstruction methods can only process simplified real genome datasets or simulated datasets with identical genome content, unique genome markers, …

Improving Rolling Bearing Fault Diagnosis By Ds Evidence Theory Based Fusion Model, Xuemei Yao, Shaobo Li, Jianjun Hu

Faculty Publications

Rolling bearing plays an important role in rotating machinery and its working condition directly affects the equipment efficiency. While dozens of methods have been proposed for real-time bearing fault diagnosis and monitoring, the fault classification accuracy of existing algorithms is still not satisfactory. This work presents a novel algorithm fusion model based on principal component analysis and Dempster-Shafer evidence theory for rolling bearing fault diagnosis. It combines the advantages of the learning vector quantization (LVQ) neural network model and the decision tree model. Experiments under three different spinning bearing speeds and two different crack sizes show that our fusion model …

The Aspergillus Flavus Homeobox Gene, Hbx1, Is Required For Development And Aflatoxin Production, Jeffrey W. Cary, Pamela Y. Harris-Coward, Leslie Scharfenstein, Brian M. Mack, Perng-Kuang Chang, Qijian Wei, Matthew Lebar, Carol Carter-Wientjes, Rajtilak Majumdar, Chandrani Mitra, Sourav Banerjee, Anindya Chanda

Faculty Publications

Homeobox proteins, a class of well conserved transcription factors, regulate the expression of targeted genes, especially those involved in development. In filamentous fungi, homeobox genes are required for normal conidiogenesis and fruiting body formation. In the present study, we identified eight homeobox (hbx) genes in the aflatoxin-producing ascomycete, Aspergillus flavus, and determined their respective role in growth, conidiation and sclerotial production. Disruption of seven of the eight genes had little to no effect on fungal growth and development. However, disruption of the homeobox gene AFLA_069100, designated as hbx1, in two morphologically different A. flavus strains, CA14 and AF70, resulted in …

Improvement Of Phylogenetic Method To Analyze Compositional Heterogeneity, Zehua Zhang, Kecheng Guo, Gaofeng Pan, Jijun Tang, Fei Guo

Faculty Publications

Background: Phylogenetic analysis is a key way to understand current research in the biological processes and detect theory in evolution of natural selection. The evolutionary relationship between species is generally reflected in the form of phylogenetic trees. Many methods for constructing phylogenetic trees, are based on the optimization criteria. We extract the biological data via modeling features, and then compare these characteristics to study the biological evolution between species.

Results: Here, we use maximum likelihood and Bayesian inference method to establish phylogenetic trees; multi-chain Markov chain Monte Carlo sampling method can be used to select optimal phylogenetic tree, resolving local …

Multiphysics Simulation Of Low-Amplitude Acoustic Wave Detection By Piezoelectric Wafer Active Sensors Validated By In-Situ Ae-Fatigue Experiment, Yeasin Bhuiyan, Victor Giurgiutiu

Faculty Publications

Piezoelectric wafer active sensors (PWAS) are commonly used for detecting Lamb waves for structural health monitoring application. However, in most applications of active sensing, the signals are of high-amplitude and easy to detect. In this article, we have shown a new avenue of using the PWAS transducer for detecting the low-amplitude fatigue-crack related acoustic emission (AE) signals. Multiphysics finite element (FE) simulations were performed with two PWAS transducers bonded to the structure. Various configurations of the sensors were studied by using the simulations. One PWAS was placed near to the fatigue-crack and the other one was placed at a certain …

Static And Dynamic Strain Monitoring Of Reinforced Concrete Components Through Embedded Carbon Nanotube Cement-Based Sensors, Antonella D’Alessandro, Filippo Ubertini, Enrique García-Macías, Rafael Castro-Triguero, Austin Downey, Simon Laflamme, Andrea Meoni, Annibale Luigi Materazzi

Faculty Publications

The paper presents a study on the use of cement-based sensors doped with carbon nanotubes as embedded smart sensors for static and dynamic strain monitoring of reinforced concrete (RC) elements. Such novel sensors can be used for the monitoring of civil infrastructures. Because they are fabricated from a structural material and are easy to utilize, these sensors can be integrated into structural elements for monitoring of different types of constructions during their service life. Despite the scientific attention that such sensors have received in recent years, further research is needed to understand (i) the repeatability and accuracy of sensors’ behavior …

Proton Transfer In Molten Lithium Carbonate: Mechanism And Kinetics By Density Functional Theory Calculations, Xueling Lei, Kevin Huang, Changyong Qin

Faculty Publications

Using static and dynamic density functional theory (DFT) methods with a cluster model of [(Li2CO3)8H]+, the mechanism and kinetics of proton transfer in lithium molten carbonate (MC) were investigated. The migration of proton prefers an inter-carbonate pathway with an energy barrier of 8.0 kcal/mol at the B3LYP/6-31 G(d,p) level, which is in good agreement with the value of 7.6 kcal/mol and 7.5 kcal/mol from experiment and FPMD simulation, respectively. At transition state (TS), a linkage of O–H–O involving O 2p and H 1 s orbitals is formed between two carbonate ions. The calculated trajectory of H indicates that proton has …

Spatial Variation Analysis For Measured Indoor Massive Mimo Channels, Qi Wang, Bo Ai, David W. Matolak, Ruisi He, Ke Guan, Zhangdui Zhong, Dapeng Li

Faculty Publications

As one of the most important candidate technologies for the fifth-generation wireless communication systems, massive MIMO technology has been widely studied recently because of the significant improvements it can provide in terms of spectrum efficiency and power efficiency. As the foundation of wireless communication, research on propagation characteristics for massive MIMO channels is of primary importance. This paper investigates the characteristics for massive MIMO channels in an indoor hall scenario at 6-GHz. Channel measurements were conducted with a bandwidth of 200 MHz in both line of sight (LOS) and non-LOS (NLOS) conditions. The statistical parameters in the delay domain were …

An Ameliorated Prediction Of Drug–Target Interactions Based On Multi-Scale Discrete Wavelet Transform And Network Features, Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo

Faculty Publications

The prediction of drug–target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug–target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns. Simultaneously, we apply the discrete wavelet transform (DWT) to extract features from target sequences. Then, we concatenate and normalize the target, drug, …

An Ensemble Deep Convolutional Neural Network Model With Improved D-S Evidence Fusion For Bearing Fault Diagnosis, Shaobo Li, Guoka Liu, Xianghong Tang, Jianguang Lu, Jianjun Hu

Faculty Publications

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations …

An Ensemble Deep Convolutional Neural Network Model With Improved D-S Evidence Fusion For Bearing Fault Diagnosis, Shaobo Li, Guokai Liu, Xianghong Tang, Jianguang Lu, Jianjun Hu

Faculty Publications

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations …

An Advanced Multi-Sensor Acousto-Ultrasonic Structural Health Monitoring System: Development And Aerospace Demonstration, Joel Smithard, Nik Rajic, Stephen Van Der Velden, Patrick Norman, Cedric Rosalie, Steve Galea, Hanfei Mei, Bin Lin, Victor Giurgiutiu

Faculty Publications

A key longstanding objective of the Structural Health Monitoring (SHM) research community is to enable the embedment of SHM systems in high value assets like aircraft to provide on-demand damage detection and evaluation. As against traditional non-destructive inspection hardware, embedded SHM systems must be compact, lightweight, low-power and sufficiently robust to survive exposure to severe in-flight operating conditions. Typical Commercial-Off-The-Shelf (COTS) systems can be bulky, costly and are often inflexible in their configuration and/or scalability, which militates against in-service deployment. Advances in electronics have resulted in ever smaller, cheaper and more reliable components that facilitate the development of compact and …

Investigation Of Mimo Channel Characteristics In A Twosection Tunnel At 1.4725 Ghz, Rongchen Sun, David W. Matolak, Cheng Tao, Liu Liu, Zhenhui Tan, Tao Zhou

Faculty Publications

This paper presents results from a wide band single-input–single-output (SISO) and 16 16 virtual multiple-input–multiple-output (MIMO) measurement campaign at a center frequency of 1.4725 GHz in a 100-meter long tunnel laboratory which is terminated by a vertical wall with a metallic door. The path loss, root-mean-square delay spread (RMS-DS) characteristics, and power delay profiles (PDPs) are described. In addition, we provide results for the MIMO channel amplitude matrix, which offers a new perspective in understanding MIMO characteristics in tunnel scenarios. Our measurement results are analyzed and compared to ray tracing simulations. The relationships among the angle spread, channel matrix singular …

Analysis Of An Upper Bound On The Effects Of Large Scale Attenuation On Uplink Transmission Performance For Massive Mimo Systems, Liu Liu, David W. Matolak, Cheng Tao, Yongzhi Li

Faculty Publications

Massive multiple-input multiple-output (MIMO) is a potential candidate key technology for the 5G of wireless communication systems. In research to date, different power loss and shadowing effects on different antenna elements across the large arrays have been neglected. In this paper, based on an idealized propagation model, a new large scale attenuation (LSA) model is proposed, by which the large scale losses (path loss and shadowing effect) over the antenna array can be considered when establishing a massive MIMO channel model. By using this model, the spectral efficiency (in terms of bits/s/Hz sum-rate) of the maximum ratio combining (MRC) detector …

Analysis Of An Upper Bound On The Effects Of Large Scale Attenuation On Uplink Transmission Performance For Massive Mimo Systems, Liu Liu, David W. Matolak, Cheng Tao, Yongzhi Li

Faculty Publications

Massive multiple-input multiple-output (MIMO) is a potential candidate key technology for the 5G of wireless communication systems. In research to date, different power loss and shadowing effects on different antenna elements across the large arrays have been neglected. In this paper, based on an idealized propagation model, a new large scale attenuation (LSA) model is proposed, by which the large scale losses (path loss and shadowing effect) over the antenna array can be considered when establishing a massive MIMO channel model. By using this model, the spectral efficiency (in terms of bits/s/Hz sum-rate) of the maximum ratio combining (MRC) detector …

Analysis Of Co-Associated Transcription Factors Via Ordered Adjacency Differences On Motif Distribution, Gaofeng Pan, Jijun Tang, Fei Guo

Faculty Publications

Transcription factors (TFs) binding to specific DNA sequences or motifs, are elementary to the regulation of transcription. The gene is regulated by a combination of TFs in close proximity. Analysis of co-TFs is an important problem in understanding the mechanism of transcriptional regulation. Recently, ChIP-seq in mapping TF provides a large amount of experimental data to analyze co-TFs. Several studies show that if two TFs are co-associated, the relative distance between TFs exhibits a peak-like distribution. In order to analyze co-TFs, we develop a novel method to evaluate the associated situation between TFs. We design an adjacency score based on …