Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

In the new millennium, with chaotic situation that exist in the world, people are threaten with multifarious terrorist attacks. There have been several intelligent ways in order to recognize and diminish these assaults wisely. Biometric traits have proven to be one of the useful ways for tackling these problems. Among all these traits, the iris recognition systems are appropriate tools for the human identification not only the iris pattern is well-known features, but also it has numerous features such as compactness representation, uniqueness texture, and stability. In spite of the fact that there have been published many approaches in these areas, there are still abundant problems in these approaches like time consuming, and computational complexity. In order to solve these obstacles, we propose the extravagant iris recognition methods that are based on combination of two dimensional Gabor kernel (2-DGK), step filtering (SF) and polynomial filtering (PF) for feature extraction and hybrid radial basis function neural network (RBFNN) with genetic algorithm (GA) for matching task. To assess the performance of the proposed method, we use two benchmarks in our algorithm and implemented it on CASIA-Iris V3, UBIRIS. V1 and UCI machine learning repository datasets. The experimental results of the proposed method reveal that the method is efficient in the iris recognition.

An intelligent method for iris recognition using supervised machine learning techniques

Ultrasonic phased array techniques are widely used for defect detection in structural health monitoring field. The increase in the element number, however, leads to larger amounts of data acquired ...

Compressive sensing of phased array ultrasonic signal in defect detection: Simulation study and experimental verification

Embracing the fact that one can recover certain signals and images from far fewer measurements than traditional methods use, compressive sensing (CS) provides solutions to huge amounts of data collection in phased array-based material characterization. This article describes how a CS framework can be utilized to effectively compress ultrasonic phased array images in time and frequency domains. By projecting the image onto its Discrete Cosine transform domain, a novel scheme was implemented to verify the potentiality of CS for data reduction, as well as to explore its reconstruction accuracy. The results from CIVA simulations indicate that both time and frequency domain CS can accurately reconstruct array images using samples less than the minimum requirements of the Nyquist theorem. For experimental verification of three types of artificial flaws, although a considerable data reduction can be achieved with defects clearly preserved, it is currently impossible to break Nyquist limitation in the time domain. Fortunately, qualified recovery in the frequency domain makes it happen, meaning a real breakthrough for phased array image reconstruction. As a case study, the proposed CS procedure is applied to the inspection of an engine cylinder cavity containing different pit defects and the results show that orthogonal matching pursuit (OMP)-based CS guarantees the performance for real application.

https://www.mdpi.com/1424-8220/18/5/1460/pdf

Ultrasonic Phased Array Compressive Imaging in Time and Frequency Domain: Simulation, Experimental Verification and Real Application

This paper presents a novel model for ultrasonic defect identification relying on multi-feature fusion and multi-criteria feature evaluation (MFF-MCFE). Based on feature extraction, feature selecti...

Defect Identification of Pipeline Ultrasonic Inspection Based on Multi-Feature Fusion and Multi-Criteria Feature Evaluation

In the automotive remanufacturing movement, the inspection of the corrosion defects on the engine cylinder cavity is a key and difficult problem. In this article, based on the ultrasonic phased arr...

Lecheng Jia

Papers

Quantitative analysis of pit defects in an automobile engine cylinder cavity using the radial basis function neural network–genetic algorithm model: