High-Sensitivity Ultrasonic Guided Wave Monitoring of Pipe Defects Using Adaptive Principal Component Analysis

Ultrasonic guided wave monitoring is regularly used for monitoring the structural health of industrial pipes, but small defects are difficult to identify owing to the influence of the environment and pipe structure on the guided wave signal. In this paper, a high-sensitivity monitoring algorithm based on adaptive principal component analysis (APCA) for defects of pipes is proposed, which calculates the sensitivity index of the signals and optimizes the process of selecting principal components in principal component analysis (PCA). Furthermore, we established a comprehensive damage index (K) by extracting the subspace features of signals to display the existence of defects intuitively. The damage monitoring algorithm was tested by the dataset collected from several pipe types, and the experimental results show that the APCA method can monitor the hole defect of 0.075% cross section loss ratio (SLR) on the straight pipe, 0.15% SLR on the spiral pipe, and 0.18% SLR on the bent pipe, which is superior to conventional methods such as optimal baseline subtraction (OBS) and average Euclidean distance (AED). The results of the damage index curve obtained by the algorithm clearly showed the change trend of defects; moreover, the contribution rate of the K index roughly showed the location of the defects.

Citizen Unique ID Based Real Time Face Recognition for Surveillance and National Security

Provision of home security services has become an integral part of our lives in today’s technological society where attackers usually have all the necessary means and resources at their disposal. Face Recognition is producing gigantic enthusiasm because of government worries about character the executives and worldwide fear based oppressor movement. One aspiration of Intelligent CCTV is to help counteract fear based oppression and a key innovation is solid face acknowledgment. The movement discovery module is dependable to decide the degree of action while the face identification module separates between approved individuals and interlopers. Our system is better than many proposed systems as it combines both motion detection and face recognition in a single system. Our framework has three noteworthy segments containing: 1) a Viola-Jones face discovery module 2) a Pose Normalization Module to evaluate facial posture and make up for extraordinary posture points 3) Adaptive Principal Component Analysis to perceive the standardized appearances.

Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.

Adaptive Principal Component Analysis-Based Outliers Detection Through Neighborhood Voting in Wireless Sensor Networks

This chapter introduces statistical learning methods and findings of a group decision-making algorithm in internet of things (IoT) and edge computing environments. The discussed methodology locally detects outliers in an on-line and adaptive mode. It is driven by three perspectives—opinion, confidence, and independence—and exploits the incremental principal component analysis using the power method for eigenvector and eigenvalue estimation and Knuth and Welford's online algorithms for variance estimation. The methodology is implemented and evaluated over real contextual data in a wireless network of environmental sensors from where appropriate conclusions are drawn about the capabilities and limitations of the proposed solution in IoT environments.