A Study on the Laser Removal of Epoxy Coatings on SS400 Surface by Beam Scanning Patterns

Shipyards are very interested in improving their working environment and resolving environmental pollution issues by replacing mechanical cleaning technologies used before and after painting processes with laser cleaning technology. Because epoxy paint is thickly coated, with a thickness of 200 μm or greater, it is difficult to remove using both laser cleaning and mechanical cleaning technologies. Therefore, this study tried to obtain effective cleaning results by controlling the process parameters when removing the thick epoxy coating using a Q-switching fiber laser cleaning system with an average power of 100 W developed by our research team. The pulse duration time of the laser is 150 ns. Additionally, in order to determine whether the cleaning was sufficient, the difference in laser-induced plume/plasma was compared. By controlling the beam scanning patterns, line overlap rate, and pulse overlap rate, it was possible to obtain effective cleaning results without introducing removal deviation. In addition, the NOP increased when the laser beam overlap rate increased. This increased the amount of heat input to the material and reduced the number of scans required to remove the epoxy paint. As a result of the plume/plasma analysis, less plume/plasma was generated as the paint was removed if the epoxy paint remained on the surface. On the other hand, when all of the paint was removed, a higher brightness of plume/plasma generated by evaporation of the bare metal was observed.

Effects of Laser Shock Peening on Microstructure and Properties in 32CrNi3MoVE Steel

The effects of laser shock peening (LSP) on microstructure and properties of 32CrNi3MoVE steel are investigated. Laser shock peening experiment was undertaken using a laser system with the pulse-width of 15ns, 50% overlap rate, and pulse-energy of 7J. The microhardness, residual stress, microstructure, and wear resistance of the laser shock peening 32CrNi3MoVE steel samples were measured. The results show that the microhardness and residual compressive stress distribution is remarkably improved by LSP. High-density dislocations are generated and the width of the martensite grains is reduced to a certain extent, indicating the phenomenon of grain compression. The friction coefficient and wear rate of the material reduces so that the wear resistance is correspondingly improved. The wear mechanism of the untreated sample is abrasive wear and adhesive wear, the wear mechanism of the treated samples is abrasive wear.

3D segmentation of lungs with juxta-pleural tumor using the improved active shape model approach

BACKGROUND AND OBJECTIVE: At present, there are many methods for pathological lung segmentation. However, there are still two unresolved problems. (1) The search steps in traditional ASM is a least square optimization method, which is sensitive to outlier marker points, and it makes the profile update to the transition area in the middle of normal lung tissue and tumor rather than a true lung contour. (2) If the noise images exist in the training dataset, the corrected shape model cannot be constructed. METHODS: To solve the first problem, we proposed a new ASM algorithm. Firstly, we detected these outlier marker points by a distance method, and then the different searching functions to the abnormal and normal marker points are applied. To solve the second problem, robust principal component analysis (RPCA) of low rank theory can remove noise, so the proposed method combines RPCA instead of PCA with ASM to solve this problem. Low rank decompose for marker points matrix of training dataset and covariance matrix of PCA will be done before segmentation using ASM. RESULTS: Using the proposed method to segment 122 lung images with juxta-pleural tumors of EMPIRE10 database, got the overlap rate with the gold standard as 94.5%. While the accuracy of ASM based on PCA is only 69.5%. CONCLUSIONS: The results showed that when the noise sample is contained in the training sample set, a good segmentation result for the lungs with juxta-pleural tumors can be obtained by the ASM based on RPCA.

Robust Visual Tracking Based on Convolutional Sparse Coding

This paper proposes a new visual tracking method by constructing the robust appearance model of the target with convolutional sparse coding. First, our method uses convolutional sparse coding to divide the interest region of the target into a smooth image and four detail images with different fitting degrees. Second, we compute the initial target region by tracking the smooth image with the kernel correlation filtering. We define an appearance model to describe the details of the target based on the initial target region and the combination of four detail images. Third, we propose a matching method by the overlap rate and Euclidean distance to evaluate candidates and the appearance model to compute the tracking results based on detail images. Finally, the two tracking results are separately computed by the smooth image, and the detail images are combined to produce the final target rectangle. Many experiments on videos from Tracking Benchmark 2015 demonstrate that our method produces much better results than most of the present visual tracking methods.

Application of Vibration Signals for the Quantitative Analysis of the Optimal Threshold of Bearing Failure

This study established the prognostics and health management system for bearing failure. The vibration signals measured during the bearing operation were used for prognostics. First, the time-domain signal of vibration was calculated through generalized fractal dimensions, and the relationship diagram of generalized fractal dimensions and time was obtained. Then, the trend of bearing failure was compared by the GFDal results. However, the results can only be used for qualitative feature extraction. The bearing failure at the beginning cannot be determined by qualitative methods. Therefore, this study further converted the calculation results of GFDs into a Gauss distribution curve based on the statistical method under normal operation of the bearing. The Gauss distribution curve of the bearing under normal operation and at different time was overlapped. The overlap rate of the bearing area under different times was calculated. The minimum value was taken as the diagnostic standard, which was the optimal threshold of bearing failure defined in this study and was used as the quantitative basis for bearing failure. Therefore, the comparison of the area overlap rate under the Gauss distribution curve between the normal bearing and the bearing under test could provide diagnosis to the bearing failure. Moreover, the time point of the initial failure of the bearing could also be estimated based on the optimal failure threshold.

Tracing the Path Toward Self-Regulated Revision: An Interplay of Instructor Feedback, Peer Feedback, and Revision Goals

Building upon Zimmerman’s socio-cognitive view of self-regulation, we explored EFL (English as a Foreign Language) students’ revision and the likely contribution to revision from three salient self-regulating sources: peer feedback, instructor feedback, and revision goals. Data was obtained from 70 Chinese EFL students in a writing class through a 300-word online writing assignment involving online instructor and peer feedback, free-response revision goals, and a required revision. We closely coded students’ revision and then used the same coding scheme to analyze the relative levels of association of revision changes with peer comments, instructor comments and revision goals. We found that: (a) the majority of revision changes have been triggered by three mediating sources, with revision goals as the most significant contributing source. Additionally, most revision changes come from a combination of two or three sources, with the overlap of peer feedback and revision goals accounting for the biggest overlapping contribution for both high and low-level revisions; (b) as for the relationship among the three sources, no significant difference was found between revision goals’ overlap rate with peer feedback and their overlap rate with instructor feedback. Instructor feedback and peer feedback did not overlap very much. Findings suggest that students could revise beyond instructor and peer feedback in their revision efforts guided by their own reflective goals, and peer feedback could function as a more productive and multiple-reader source of revision in comparison with instructor feedback. This study also provided evidence for students’ self-regulated learning of writing through the use of self-regulating resources and charted a route for how writing could be improved.

Clonal diversity of the B cell receptor repertoire in patients with coronary in-stent restenosis and type 2 diabetes

Type 2 diabetes mellitus (T2DM) is known as a risk factor for coronary in-stent restenosis (ISR) in patients with coronary artery disease (CAD). Evidence suggests that B cells play a functional role in the progression of atherosclerotic lesions. However, the B cell receptor (BCR) repertoire in patients with ISR remains unclear. This study aims to profile the BCR repertoire in patients with coronary ISR/T2DM. A total of 21 CAD patients with or without ISR/T2DM were enrolled. PBMCs were isolated and examined for BCR repertoire profiles using DNA-seq. Our results showed that the diversity of amino acid sequences in ISR DM patients was higher than that in ISR −DM patients. The frequencies of 21 V/J paired genes differed between ISR DM and −ISR DM patients, while frequencies of 5 V/J paired genes differed between ISR DM and ISR −DM. The −ISR −DM group presented the highest clonotype overlap rate, while ISR DM patients presented the lowest overlap rate. Our study presented the BCR repertoires in patients with ISR/T2DM. The data suggested different BCR signatures between patients with ISR and T2DM. Further analysis of BCR profiles would enhance understanding of ISR.

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.