Application and Challenges of Signal Processing Techniques for Lamb Waves Structural Integrity Evaluation: Part A-Lamb Waves Signals Emitting and Optimization Techniques

Lamb waves have been widely studied in structural integrity evaluation during the past decades with their low-attenuation and multi-defects sensitive nature. The performance of the evaluation has close relationship with the vibration property and the frequency of Lamb waves signals. Influenced by the nature of Lamb waves and the environment, the received signals may be difficult to interpret that limits the performance of the detection. So pure Lamb waves mode emitting and high-resolution signals acquisition play important roles in Lamb waves structural integrity evaluation. In this chapter, the basic theory of Lamb waves nature and some environment factors that should be considered in structural integrity evaluation are introduced. Three kinds of typical transduces used for specific Lamb waves mode emitting and sensing are briefly introduced. Then the development of techniques to improve the interpretability of signals are discussed, including the waveform modulation techniques, multi-scale analysis techniques and the temperature effect compensation techniques are summarized

Application and Challenges of Signal Processing Techniques for Lamb Waves Structural Integrity Evaluation: Part B-Defects Imaging and Recognition Techniques

The wavefield of Lamb waves is yielded by the feature of plate-like structures. And many defects imaging techniques and intelligent recognition algorithms have been developed for defects location, sizing and recognition through analyzing the parameters of received Lamb waves signals including the arrival time, attenuation, amplitude and phase, etc. In this chapter, we give a briefly review about the defects imaging techniques and the intelligent recognition algorithms. Considering the available parameters of Lamb waves signals and the setting of detection/monitoring systems, we roughly divide the defect location and sizing techniques into four categories, including the sparse array imaging techniques, the tomography techniques, the compact array techniques, and full wavefield imaging techniques. The principle of them is introduced. Meanwhile, the intelligent recognition techniques based on various of intelligent recognition algorithms that have been widely used to analyze Lamb waves signals in the research of defect recognition are reviewed, including the support vector machine, Bayesian methodology, and the neural networks

Coadjoint orbits of Lie groupoids

For a Lie groupoid $\mathcal{G}$ with Lie algebroid $A$, we realize the symplectic leaves of the Lie-Poisson structure on $A^*$ as orbits of the affine coadjoint action of the Lie groupoid $\mathcal{J}\mathcal{G}\ltimes T^*M$ on $A^*$, which coincide with the groupoid orbits of the symplectic groupoid $T^*\mathcal{G}$ over $A^*$. It is also shown that there is a fiber bundle structure on each symplectic leaf. In the case of gauge groupoids, a symplectic leaf is the universal phase space for a classical particle in a Yang-Mills field

The Atiyah class of generalized holomorphic vector bundles

For a generalized holomorphic vector bundle, we introduce the Atiyah class, which is the obstruction of the existence of generalized holomorphic connections on this bundle. Similar to the holomorphic case, such Atiyah classes can be defined by three approaches: the $\rm{\check{C}}$ech cohomology, the extension class of the first jet bundle as well as the Lie pair

Mining Patterns and Networks from Sequence Data

Sequence data are ubiquitous in diverse domains such as bioinformatics, computational neuroscience, and user behavior analysis. As a result, many critical applications require extracting knowledge from sequences in multi-level. For example, mining frequent patterns is the central goal of motif discovery in biological sequences, while in computational neuronal science, one essential task is to infer causal networks from neural event sequences (spike trains). Given the wide application of pattern and network mining tools for sequence data, they are facing new challenges posted by modern instruments. That is, as large scale and high resolution sequence data become available, we need new methods with better efficiency and higher accuracy.In this dissertation, we propose several approaches to improve existing pattern and network mining tools to meet new challenges in terms of efficiency and accuracy. The first problem is how to scale existing motif discovery algorithms. Our work on motif discovery focuses on the challenge of discovering motifs from a large scale of short sequences that none of existing motif finding algorithms can handle. We propose an anchor based clustering algorithm that could significantly improve the scalability of all the existing motif finding algorithms without losing accuracy at all. In particular, our algorithm could reduce the running time of a very popular motif finding algorithm, MEME, from weeks to a few minutes with even better accuracy.In another work, we study the problem of how to accurately infer a functional network from neural recordings (spike trains), which is an essential task in many real world applications such as diagnosing neurodegenerative diseases. We introduce a statistical tool that could be used to accurately identify inhibitory causal relations from spike trains. While most of existing works devote their efforts on characterizing the statistics of neural spike trains, we show that it is crucial to make predictions about the response of neurons to changes. More importantly, our results are validated by real biological experiments with a novel instrument, which makes this work the first of its kind. Furthermore, while most existing methods focus on learning functional networks from purely observational data, we propose an active learning framework that could intelligently generate and utilize interventional data. We demonstrate that by intelligently adopting interventional data using the active learning models we propose, the accuracy of the inferred functional network could be substantially improved with the same amount of training data

Sudanske dvosupnice kao alternativni izvor vlakana za proizvodnju celuloze i papira

The suitability of the stems from two Sudanese dicotyledonous annual plants, namely castor bean (Ricinus communis) and Leptadenia pyrotechnica (L. pyrotechnica) were investigated for pulp and papermaking. Chemical compositions, elemental analysis, fiber dimensions, paper physical properties and morphology revealed a relatively high α-cellulose content (46.2 and 44.3 %) and low lignin (19.7 and 21.7 %) in the stems of castor bean and L. pyrotechnica, respectively. The average fiber length of castor bean and L. pyrotechnica is 0.80 and 0.70 mm with fiber width of 16.30 μm and 18.20 μm, respectively, which makes them acceptable candidates. Soda-AQ pulping of castor bean stem led to a higher pulp yield of 43.2 % at kappa number 18.2 compared to 40.3 % at kappa 20.3 for L. pyrotechnica. This yield is less than that obtained for wood plants and similar to that observed for annual plants. Paper handsheets produced from castor bean showed better mechanical properties than L. pyrotechnica. SEM images indicated that the produced papers were quite homogeneous, compact, closely packed, and well assembled.U radu je opisano istraživanje prikladnosti stabljika dviju sudanskih jednogodišnjih biljaka dvosupnica, ricinusa (Ricinus communis) i Leptadenia pirotechnica za dobivanje celulozu i proizvodnju papira. Kemijski sastav, elementarna analiza, dimenzije vlakana, fizikalna svojstva papira i morfologija pokazali su da je u stabljikama ricinusa i L. pyrotechnica relativno visok sadržaj α-celuloze (46,2 i 44,3 %) i nizak sadržaj lignina (19,7 i 21,7 %). Prosječna duljina vlakana ricinusa i L. pirotechnice iznosi 0,80 i 0,70 mm, a širina vlakana im je od 16,30 μm i 18,20 μm, što ih čini prihvatljivima za proizvodnju celuloze i papira. Sulfatnim je postupkom od stabljika ricinusa dobiven veći prinos celuloze (43,2 %) pri kappa broju 18,2 u usporedbi s prinosom celuloze (40,3 %) pri kappa broju 20,3, koji je dobiven pri proizvodnji celuloze od stabljika L. pyrotechnice. Dobiveni prinos manji je od prinosa koji se postiže proizvodnjom celuloze od drvenastih biljaka i jednak je prinosu koji se ostvaruje proizvodnjom celuloze od jednogodišnjih biljaka. Listovi papira proizvedeni od ricinusa pokazali su bolja mehanička svojstva od papira proizvedenoga od stabljika L. pyrotechnice. Slike dobivene skenirajućim elektronskim mikroskopom (SEM) pokazuju da su proizvedeni papiri bili posve homogeni, kompaktni, zbijeni i dobro sastavljeni