A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns

Monitoring of modal frequencies under an unsupervised learning framework is a practical strategy for damage assessment of civil structures, especially bridges. However, the key challenge is related to high sensitivity of modal frequencies to environmental and/or operational changes that may lead to economic and safety losses. The other challenge pertains to different environmental and/or operational variation patterns in modal frequencies due to differences in structural types, materials, and applications, measurement periods in terms of short and/or long monitoring programs, geographical locations of structures, weather conditions, and influences of single or multiple environmental and/or operational factors, which may cause barriers to employing stateof-the-art unsupervised learning approaches. To cope with these issues, this paper proposes a novel double-hybrid learning technique in an unsupervised manner. It contains two stages of data partitioning and anomaly detection, both of which comprise two hybrid algorithms. For the first stage, an improved hybrid clustering method based on a coupling of shared nearest neighbor searching and density peaks clustering is proposed to prepare local information for anomaly detection with the focus on mitigating environmental and/or operational effects. For the second stage, this paper proposes an innovative non-parametric hybrid anomaly detector based on local outlier factor. In both stages, the number of nearest neighbors is the key hyperparameter that is automatically determined by leveraging a self-adaptive neighbor searching algorithm. Modal frequencies of two full-scale bridges are utilized to validate the proposed technique with several comparisons. Results indicate that this technique is able to successfully eliminate different environmental and/or operational variations and correctly detect damage

The Effects of Signal and Image Compression of SAR Data on Change Detection Algorithms

With massive amounts of SAR imagery and data being collected, the need for effective compression techniques is growing. One of the most popular applications for remote sensing is change detection, which compares two geo-registered images for changes in the scene. While lossless compression is needed for signal compression, the same is not often required for image compression. In almost every case the compression ratios are much higher in lossy compression making them more appealing when bandwidth and storage becomes an issue. This research analyzes different types of compression techniques that are adapted for SAR imagery, and tests these techniques with three different change detection algorithms. Many algorithms exist that allow large compression ratios, however, the usefulness of the data is always the final concern. It is necessary to identify compression methods that will not degrade the performance of change detection analysis

Evaluation of an Outer Loop Retrofit Architecture for Intelligent Turbofan Engine Thrust Control

The thrust control capability of a retrofit architecture for intelligent turbofan engine control and diagnostics is evaluated. The focus of the study is on the portion of the hierarchical architecture that performs thrust estimation and outer loop thrust control. The inner loop controls fan speed so the outer loop automatically adjusts the engine's fan speed command to maintain thrust at the desired level, based on pilot input, even as the engine deteriorates with use. The thrust estimation accuracy is assessed under nominal and deteriorated conditions at multiple operating points, and the closed loop thrust control performance is studied, all in a complex real-time nonlinear turbofan engine simulation test bed. The estimation capability, thrust response, and robustness to uncertainty in the form of engine degradation are evaluated

Sensemaking of narratives: informing the capabilities development process

This capstone project determines whether sensemaking of soldier narratives can inform the Department of Defense‘s (DOD) capability development process (CDP). Sensemaking is the process of creating awareness and understanding in situations of high complexity or uncertainty. The authors gathered service member narratives concerning their use of fielded equipment, which created metadata for both quantitative and qualitative research and analysis. This capstone compares results from sensemaking of narratives with results from the Warfighter Technology Tradespace Methodology (WTTM), a system designed for the rapid fielding of equipment for small forward operating bases (FOBs) and combat outposts (COPs). The capstone finds that 1) soldier narratives inform the fielding process by providing an additional layer of meaning and context, and 2) soldier narratives do not replace current feedback mechanisms; rather, they play a complementary role. This capstone finds that narratives as a feedback mechanism can be applied during operational testing of newly developed or fielded equipment for the DOD‘s CDP.http://archive.org/details/sensemakingofnar1094542657Major, United States Army;Major, United States Army;Major, United States ArmyApproved for public release; distribution is unlimited

Mitigating Denial of Service Attacks in Fog-Based Wireless Sensor Networks Using Machine Learning Techniques

Wireless sensor networks are considered to be among the most significant and innovative technologies in the 21st century due to their wide range of industrial applications. Sensor nodes in these networks are susceptible to a variety of assaults due to their special qualities and method of deployment. In WSNs, denial of service attacks are common attacks in sensor networks. It is difficult to design a detection and prevention system that would effectively reduce the impact of these attacks on WSNs. In order to identify assaults on WSNs, this study suggests using two machine learning models: decision trees and XGBoost. The WSNs dataset was the subject of extensive tests to identify denial of service attacks. The experimental findings demonstrate that the XGBoost model, when applied to the entire dataset, has a higher true positive rate (98.3%) than the Decision tree approach (97.3%) and a lower false positive rate (1.7%) than the Decision tree technique (2.7%). Like this, with selected dataset assaults, the XGBoost approach has a higher true positive rate (99.01%) than the Decision tree technique (97.50%) and a lower false positive rate (0.99%) than the Decision tree technique (2.50%)