Integration of Sparse Coding-Based Vibration Signal Extraction Techniques in Precision Agriculture for Enhanced Monitoring of Biotechnologically-Optimized Marine Platform Truss Structures.

Abstract

The intersection of disparate scientific fields often catalyzes remarkable advancements, leading to innovative solutions for complex challenges. In this context, we explore the integration of sparse coding-based vibration signal extraction techniques within the framework of marine platform truss structures and their relevance in the domain of biotechnology-driven agriculture. Marine platform truss structures, vital components of offshore installations, are subject to dynamic forces that can impact their stability and performance. Sparse coding-based vibration signal extraction methods, initially devised for structural health monitoring, offer a unique approach to precisely assess these structures' dynamics. The sampled vibration signals in the frequency domain are divided into two parts. One, the nonzero elements, is all the information of the original signal, and the other one is more difficult to extract from the original signal because of its high level noise present in it. The first part includes both linear and nonlinear components that are only present at a certain spatial location while they have no effect on others. In order to identify these components, they need to propagate through scatterers with an appropriate amount of time delay before arriving at their destination point without any significant change in amplitude or phase as long as these processes are smooth enough. Based on this assumption, we present the outline of a new approach to the extraction of these components. More specifically, we propose constructing a sparse representation of the signal by grouping its elements. Afterward, we apply the principle of maximum mutual information between all pairs of them to estimate their statistical distributions and to remove all nonzero ones using perceptron mechanism with a soft thresholding. Finally, by computing integrals over all these distributions in time domain, an estimate can be obtained for each component whose dimensionality is smaller than that expected by Gaussian distribution.