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Summary This paper presents the development of a fatigue damage detection and growth monitoring technique based on coda wave interferometry (CWI) for composite structures. The time domain distortion (TD) component of coda waves, which is typically ignored as noise in the literature, is used and experimentally validated. First, a widely used signal processing method for CWI called the stretching method is applied, and TD component is extracted in the time domain. Next, threshold values are statistically estimated using noise levels of the signal and compared with the amplitude of TD component for detecting early‐stage fatigue damage. A damage index (DI) is then obtained from TD component and used to monitor fatigue damage growth. The experimental results of the damage detection and growth monitoring are validated through visual inspection during the fatigue test. Finally, the developed technique's performance is evaluated by comparing it to conventional ultrasonic and CWI‐based composite fatigue damage assessment methods. The uniqueness of this study lies in (1) detection and growth monitoring of the early‐stage composite fatigue damage, (2) extraction and use of TD component of CWI and investigation of the correlation with the damage existence and growth, and (3) experimental validation and performance examination by comparing with the conventional ultrasonic and CWI‐based composite fatigue damage assessment methods.

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Three-dimensional chromatin structures regulate gene expression across genome. The significance of de novo mutations (DNMs) affecting chromatin interactions in autism spectrum disorder (ASD) remains poorly understood. We generated 931 whole-genome sequences for Korean simplex families to detect DNMs and identified target genes dysregulated by noncoding DNMs via long-range chromatin interactions between regulatory elements. Notably, noncoding DNMs that affect chromatin interactions exhibited transcriptional dysregulation implicated in ASD risks. Correspondingly, target genes were significantly involved in histone modification, prenatal brain development, and pregnancy. Both noncoding and coding DNMs collectively contributed to low IQ in ASD. Indeed, noncoding DNMs resulted in alterations, via chromatin interactions, in target gene expression in primitive neural stem cells derived from human induced pluripotent stem cells from an ASD subject. The emerging neurodevelopmental genes, not previously implicated in ASD, include CTNNA2, GRB10, IKZF1, PDE3B, and BACE1.Our results were reproducible in 517 probands from MSSNG cohort. This work demonstrates that noncoding DNMs contribute to ASD via chromatin interactions. probands=29%, MSSNG probands=28%, and Korean siblings=21%) ( Supplementary Fig. 2c).To validate those chromatin interactions within fetal human brains, we utilized published Hi-C data of neuronal progenitor cells 11 as a baseline with which to compare Hi-C interactions for the NCD genes (see Methods). Reflecting the intensities of the Hi-C interactions, distancenormalized interaction frequencies for the proband NCD genes were significantly higher than those at baseline (Korean probands P=2.3x10 -4 , MSSNG probands P=5.0x10 -8 , Student's twosided t-test) ( Fig. 1b and Supplementary Fig. 2d). These results indicated that the chromatin interactions between NCD genes and noncoding DNMs were validated using the independent Hi-C data.Examining distances of the chromatin interactions, we observed, in the probands, 269 of the 363 NCD genes (74.1%) were discordant to genes nearest to the 230 DNMs (Fig. 1c). Moreover, 232 of the 363 NCD genes (63.9%) in the probands showed chromatin interactions over a distance of 100kb-500kb, whereas only 23 of the 129 genes nearest to the noncoding DNMs (17.8%) showed interactions over the same distance (P=2.4x10 -19 , Proportion test) ( Fig. 1c and Supplementary Fig. 2e). These results suggested that majority of the noncoding DNMs might affect their target NCD genes via long-range chromatin interactions.We then evaluated the functional properties of the 363 NCD genes in the probands in comparison to 129 of the nearest genes in the probands and to 113 NCD genes in the siblings.After 10,000 permutations, using genes differentially expressed in brains of ASD patients 12,13 , we noted significant overlap only for the proband NCD genes (Proband NCD genes P=0.01, proband nearest genes P=0.71, and sibling NCD genes P=0.12, Random permutation test) ( Fig. 1d). Besides, the expression le...

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A real-time monitoring framework is developed to detect operational anomalies in aircraft engine performance. A historical flight dataset recorded from commercial aircraft is utilized to perform the proposed method. Sampling frequency synchronization and denoise are performed on the flight dataset using signal processing techniques. A robust detection algorithm using the deep neural network is developed to capture flight performance anomalies that show significant off-nominal behavior in engine related and flight dynamic features. The accuracy and efficiency of the proposed monitoring method are validated through a demonstration of anomaly detection in the aircraft engine system associated with dynamic flight behavior.

This paper presents a robust real-time aircraft health monitoring framework using a machine learning based approach, specifically the multivariate Gaussian mixture model (mGMM), for the detection of in-air operational anomalies of an aircraft system. Sensor fusion and noise filtering algorithms have also been adopted to reduce dimensionality of the feature space while avoiding the elimination of useful information from the original flight data. Random noise in each feature, induced by the aircraft sensors and data acquisition system, is filtered out using a weighted averaging window while maintaining inherent variances. The filtered dataset is then fused according to the underlying physics of each sensed feature to reduce redundant features and subsequently trained using the mGMM. The methodology allows monitoring the behavior of each feature as well as correlations between features, significantly improving detection sensitivity. The high computational efficiency of this approach permits real-time monitoring of an aircraft system.