Class Of Defects Research Articles

Development and focusing enhancement of nonlinear air-coupled acoustic technique for damage characterization in materials

In recent years, a number of studies within the field of structural health monitoring have focused on micro-damage location in materials. These non-invasive methods are widely used for damage characterization in materials employed in aerospace and automotive fields, such as steel, aluminum and carbon fiber–reinforced plastic (CFRP) composite. Damage such as micro cracks, layer delaminations, corrosion or barely visible impact damage (BVID) can compromise the integrity of structures. Common inspection techniques based on ultrasound cannot detect small defects. However, many techniques focused on nonlinear wave behavior have recently been developed to improve the sensitivity of ultrasonic methods. The nonlinear acoustic approaches proposed in this work rely on the production of further harmonics, which generally occur at multiples of the fundamental (driving) frequency. This phenomenon is related to the strong nonlinear dynamics of flaws due to the friction between crack surfaces. A damaged sample was excited by an Air-Coupled Ultrasound (ACU) device consisting of 88 transmitting sensors and 1 receiver element. The sensor has a central frequency of 40 KHz and focused at a certain point (F = 80 mm). Results confirmed a linear vibration of the intact part of the inspected sample, which results in no higher order harmonic production in the frequency spectrum. On the other hand, a small cracked defect behaves as an active radiation source of a new frequency, twice the value of the input frequency, known as second harmonic (2f0). For nonlinear imaging and damage characterization, the second order nonlinear parameter was studied to quantify these nonlinear features. In conclusion, this research work demonstrated that nonlinear techniques are suitable for numerous classes of defects, such as fatigue cracks and corrosion (micro-cracks).

Discriminative feature learning and cluster-based defect label reconstruction for reducing uncertainty in wafer bin map labels

Many studies have been conducted to improve wafer bin map (WBM) defect classification performance because accurate WBM classification can provide information about abnormal processes causing a decrease in yield. However, in the actual manufacturing field, the manual labeling performed by engineers leads to a high level of uncertainty. Label uncertainty has been a major cause of the reduction in WBM classification system performance. In this paper, we propose a class label reconstruction method for subdividing a defect class with various patterns into several groups, creating a new class for defect samples that cannot be categorized into known classes and detecting unknown defects. The proposed method performs discriminative feature learning of the Siamese network and repeated cross-learning of the class label reconstruction based on Gaussian means clustering in a learned feature space. We verified the proposed method using a real-world WBM dataset. In a situation where there the class labels of the training dataset were corrupted, the proposed method could increase the classification accuracy of the test dataset by enabling the corrupted sample to find its original class label. As a result, the accuracy of the proposed method was up to 7.8% higher than that of the convolutional neural network (CNN). Furthermore, through the proposed class label reconstruction, we found a new mixed-type defect class that had not been found until now, and we detected new types of unknown defects that were not used for learning with an average accuracy of over 73%.

Classification of wood defect images using local binary pattern variants

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.

Software defect prediction based on correlation weighted class association rule mining

Software defect prediction based on supervised learning plays a crucial role in guiding software testing for resource allocation. In particular, it is worth noticing that using associative classification with high accuracy and comprehensibility can predict defects. But owing to the imbalance data distribution inherent, it is easy to generate a large number of non-defective class association rules, but the defective class association rules are easily ignored. Furthermore, classical associative classification algorithms mainly measure the interestingness of rules by the occurrence frequency, such as support and confidence, without considering the importance of features, resulting in combinations of the insignificant frequent itemset. This promotes the generation of weighted associative classification. However, the feature weighting based on domain knowledge is subjective and unsuitable for a high dimensional dataset. Hence, we present a novel software defect prediction model based on correlation weighted class association rule mining (CWCAR). It leverages a multi-weighted supports-based framework rather than the traditional support-confidence approach to handle class imbalance and utilizes the correlation-based heuristic approach to assign feature weight. Besides, we also optimize the ranking, pruning and prediction stages based on weighted support. Results show that CWCAR is significantly superior to state-of-the-art classifiers in terms of Balance, MCC, and Gmean.

Revealing multiple classes of stable quantum emitters in hexagonal boron nitride with correlated optical and electron microscopy.

Defects in hexagonal boron nitride (hBN) exhibit high-brightness, room-temperature quantum emission, but their large spectral variability and unknown local structure challenge their technological utility. Here, we directly correlate hBN quantum emission with local strain using a combination of photoluminescence (PL), cathodoluminescence (CL) and nanobeam electron diffraction. Across 40 emitters, we observe zero phonon lines (ZPLs) in PL and CL ranging from 540 to 720 nm. CL mapping reveals that multiple defects and distinct defect species located within an optically diffraction-limited region can each contribute to the observed PL spectra. Local strain maps indicate that strain is not required to activate the emitters and is not solely responsible for the observed ZPL spectral range. Instead, at least four distinct defect classes are responsible for the observed emission range, and all four classes are stable upon both optical and electron illumination. Our results provide a foundation for future atomic-scale optical characterization of colour centres.

Does the Brown classification of maxillectomy defects have prognostic prediction for patients with oral cavity squamous cell carcinoma involving the maxilla?

The aim of this study was to investigate the correlation between the maxillectomy defect, T stage, and prognosis of patients with maxillary squamous cell carcinoma (SCC). The Brown classification system was used to appraise the maxillectomy defects due to maxillary SCC. The clinical data of 137 patients with maxillary SCC during the period 2000–2010 were reviewed; 105 patients were followed up. Preoperative T stage and postoperative maxillectomy class were recorded. The relationship between the maxillectomy defect class and T stage of maxillary SCC was analysed. Correlations between the maxillectomy defect class, local recurrence rate, and survival rate were assessed using IBM SPSS Statistics v19.0. The most common maxillectomy defect class was IIb (54.7%, 75/137). The maxillectomy defect class was significantly associated with the T stage (P < 0.001). Both T stage and the maxillectomy defect class were significantly associated with the survival rate of patients with maxillary SCC (both P< 0.001). In conclusion, the class of the maxillectomy defect was found to be associated with the T stage. Both of these were prognostic factors for patients with maxillary SCC. The class of the maxillectomy defect is suitable for clinical application in predicting the prognosis compared with T stage.

Real-time seam defect identification for Al alloys in robotic arc welding using optical spectroscopy and integrating learning

Accurate on-line weld defect detection in robotic arc welding manufacturing is still challenging, due to the complexity and diversity of weld defects. In this study, a new real-time defect identification method is proposed for Al alloys in robotic arc welding, using arc optical spectroscopy and an integrated learning method. Spectrum feature was extracted, based on the absolute coefficients of the principal components. Feature importance was quantitatively evaluated using the mean decrease accuracy of Principal Component Analysis-Random Forest (PCA-RF). A new indicator, e.g., Importance Factor, was proposed, based on the variance of the out-of-bag test error of RF to select the optimal feature subset. The proposed PCA-RF proved to effectively identify five classes of weld defects with better performance than support vector machine and back propagation neural network. Finally, the selection pattern of spectrum feature subset was investigated, before revealing the correlation mechanism of the selected lines spectrum and weld process.

Outcomes of Class II composite restorations placed by dental students: An observational study

Introduction: Main objectives of restoring the teeth is to remove the caries and replace the tooth structure to restore form and function. This radiographic study aimed to determine the defective Class II composite restorations in permanent teeth placed by the undergraduate dental students in a private dental school. Materials and Methods: A retrospective analysis of bitewing radiographs of Class II composite restorations placed by the clinical dental students of 2017–2018 year was assessed to determine the defective restorations. Bitewing radiographs were examined to record voids, open contact, residual caries, open margin, defective restoration, and acceptable composite Class II restorations. Results: A total of 1514 permanent teeth filled with composite Class II restorations were assessed using bitewing radiographs. Almost 935 teeth showed signs of failure of Class II restorations. Reasons for the failure of most of the Class II composite restoration were due overhang 197 (13.01%), followed by voids 184 (12.15%), open contact 167 (11%), poor contour 165 (10.88%), open margin 135 (8.91%), and residual caries 87 (5.75%). A significant difference between male and female students was observed with regard to voids (P = 0.026), overhang (P = 0.016), and open margins (P = 0.001) of the Class II composite restorations. Conclusion: Overhang was the most common, and residual caries was the least common defects observed in the bitewings. Male dental students placed higher defective composite Class II restorations compared to the female dental students.

The Fgf8 subfamily (Fgf8, Fgf17 and Fgf18) is required for closure of the embryonic ventral body wall.

ABSTRACTThe closure of the embryonic ventral body wall in amniotes is an important morphogenetic event and is essential for life. Defects in human ventral wall closure are a major class of birth defect and a significant health burden. Despite this, very little is understood about how the ventral body wall is formed. Here, we show that fibroblast growth factor (FGF) ligands FGF8, FGF17 and FGF18 are essential for this process. Conditional mouse mutants for these genes display subtle migratory defects in the abdominal muscles of the ventral body wall and an enlarged umbilical ring, through which the internal organs are extruded. By refining where and when these genes are required using different Cre lines, we show that Fgf8 and Fgf17 are required in the presomitic mesoderm, whereas Fgf18 is required in the somites. This study identifies complex and multifactorial origins of ventral wall defects and has important implications for understanding their origins during embryonic development.

A Multi-Sensor Data Fusion System for Laser Welding Process Monitoring

Most existing laser welding process monitoring (LWPM) technologies focus on detecting post-process defects. However, in sheet metal laser welding applications such as welding of electronic consumer products during mass production, in-process defect detection is more important. In this article, a compact LWPM system using multi-sensor data fusion to detect in-process defects has been built. This system can collect the time series of plasma intensity, light intensity and temperature data for feature analysis. To verify the system's effectiveness, a plasma-light-temperature dataset has been compiled, which consists of 5,836 samples of nine classes, including one positive class and eight negative classes of typical in-process defects. A multi-sensor data fusion network based on a convolution neural network for in-process defect detection, called IDDNet, has also been proposed. Experimental results have demonstrated that IDDNet can achieve better multi-classification results than the support vector machine, with an overall accuracy of 97.57%. In particular, considering this monitoring process as a binary classification problem, IDDNet can achieve a 99.42% accuracy. Moreover, IDDNet can reach an average speed of 0.79ms per sample on a single GTX 1080ti graphics card, which meets the real-time requirement for industrial production. The proposed LWPM system has been successfully verified in real applications of sheet metal laser welding.

Conformal constraints on defects

In this paper we study the constraints imposed by conformal invariance on extended objects a.k.a. defects in a conformal field theory. We identify a particularly nice class of defects that is closed under conformal transformations. Correlation function of the defect with a bulk local operator is fixed by conformal invariance up to an overall constant. This gives rise to the notion of defect expansion, where the defect itself is expanded in terms of local operators. This expansion generalizes the idea of the boundary state. We will show how one can fix the correlation function of two defects from the knowledge of the defect expansion. The defect correlator admits a number of conformal cross-ratios depending on their dimensionality. We find the differential equation obeyed by the conformal block and solve them in certain special cases.

Scattering of compactlike structures

We investigate the collision of a new class of topological defects that tends to become compact as a control parameter increases to larger and larger values. These new compactlike defects have, in general, more than one internal discrete mode depending on the value of the control parameter and, as usual, there is a critical velocity above which the defects escape after the collision. We noticed that below the critical velocity there are the windows of escape presenting fractal structure. An interesting novelty is the appearance of metastable structures with the formation of compactlike defects, maintaining a fixed distance from each other. Another new feature is the formation of boosted localized distributions of the scalar field which we called moving oscillons. These oscillons carry away almost all scalar field energy producing a complete disruption of the compactlike defects. The pattern of the moving oscillons depends on the control parameter, and becomes more complex as we increase its value. We conjecture that the new effects may be connected with the presence of more than one vibrational mode in the spectrum of the stability potential of the model under investigation.

A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals.

Effective, accurate and adequately early detection of any potential defects in power transformers is still a challenging issue. As the acoustic method is known as one of the noninvasive and nondestructive testing methods, this paper proposes a new approach of the classification method for defect identification in power transformers based on the acoustic measurements. Typical application of acoustic emission (AE) method is the detection of partial discharges (PD); however, during PD measurements other defects may also be identified in the transformer. In this research, a database of various signal sources recorded during acoustic PD measurements in real-life power transformers over several years was gathered. Furthermore, all of the signals are divided into two groups (PD/other) and in the second step into eight classes of various defects. Based on these, selected classification models including machine learning algorithms were applied to training and validation. Energy patterns based on the discrete wavelet transform (DWT) were used as model inputs. As a result, the presented method allows one to identify with high accuracy, not only the selected kind of PD (1st step), but other kinds of faults or anomalies within the transformer being tested (2nd step). The proposed two-step classification method may be applied as a supplementary part of a technical condition assessment system or decision support system for management of power transformers.

Narrow Optical Line Widths in Erbium Implanted in TiO2.

Atomic and atomlike defects in the solid state are widely explored for quantum computers, networks, and sensors. Rare earth ions are an attractive class of atomic defects that feature narrow spin and optical transitions that are isolated from the host crystal, allowing incorporation into a wide range of materials. However, the realization of long electronic spin coherence times is hampered by magnetic noise from abundant nuclear spins in the most widely studied host crystals. Here, we demonstrate that Er3+ ions can be introduced via ion implantation into TiO2, a host crystal that has not been studied extensively for rare earth ions and has a low natural abundance of nuclear spins. We observe efficient incorporation of the implanted Er3+ into the Ti4+ site (>50% yield) and measure narrow inhomogeneous spin and optical line widths (20 and 460 MHz, respectively) that are comparable to bulk-doped crystalline hosts for Er3+. This work demonstrates that ion implantation is a viable path to studying rare earth ions in new hosts and is a significant step toward realizing individually addressed rare earth ions with long spin coherence times for quantum technologies.

Stationary Quantum Vortex Street in a Driven-Dissipative Quantum Fluid of Light.

We investigate the formation of a new class of density-phase defects in a resonantly driven 2D quantum fluid of light. The system bistability allows the formation of low-density regions containing density-phase singularities confined between high-density regions. We show that, in 1D channels, an odd (1 or 3) or even (2 or 4) number of dark solitons form parallel to the channel axis in order to accommodate the phase constraint induced by the pumps in the barriers. These soliton molecules are typically unstable and evolve toward stationary symmetric or antisymmetric arrays of vortex streets straightforwardly observable in cw experiments. The flexibility of this photonic platform allows implementing more complicated potentials such as mazelike channels, with the vortex streets connecting the entrances and thus solving the maze.

Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning

In the lumber and wood processing industry, most visual quality inspections are still done by trained human operators. Visual inspection is a tedious and repetitive task that involves a high likelihood of human error. Currently, new automated solutions with high-resolution cameras and visual inspection algorithms are being tested, but they are not always fast and accurate enough for real-time industrial applications. This paper proposes an automatic visual inspection system for the location and classification of defects on the wood surface. We adopted a faster region-based convolutional neural network (faster R-CNN) for the identification of defects on wood veneer surfaces. Faster R-CNN has been successfully used in medical image processing and object tracking before, but it has not yet been applied for wood panel surface quality assurance. To improve the results, we used pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The results of the experiments using a synthetically augmented dataset are presented. The best average accuracy of 80.6% was obtained using the pretrained ResNet152 neural network model. By combining all the defect classes, a 96.1% accuracy of finding wood panel surface defects was achieved.

APRSuite: A suite of components and use cases based on categorical decomposition of automatic program repair techniques and tools

During the last decade, we are witnessing the advent of a proliferation of techniques and associated tools for automatic program repair (APR). The current techniques and tools provide rich sources of knowledge that should be taken into consideration for future research. An overview of the current APR techniques and tools can serve the research community as a knowledge accumulator. However, APR techniques and tools differ in many aspects making knowledge accumulation challenging. To overcome this challenge, in this paper, we propose to leverage common components that constitute the APR techniques and tools. To achieve this objective, we surveyed current APR techniques and tools to identify the APR Suite of common constituent components, namely as APRSuite. Repair source and defect class are examples of identified components. We grouped these components into several categories such as patch evaluation and target defects. We have also identified some of the possible use cases per component as well as different lessons learned in studies for each component and for each use case. In addition, we developed a principled way for application of the components. The APRSuite and the principled way to apply it comprise a framework for knowledge accumulation, evaluation, and comparison of APR techniques and tools. The novelty of our work lies in its original viewpoint to the process of literature review in the APR research field. To demonstrate the applicability of the framework, we mapped out several concrete APR techniques, as a first instantiation of the framework. We observed that the framework brings discipline into the evaluation and/or comparison of APR techniques and tools. The framework offers these benefits objectively and systematically. We concluded that knowledge accumulation and characterization through literature reviews can be therefore facilitated through the identified suite of components while at the same time the existing component suite can be modified, augmented, or improved.

Investigating the Effect of the Presence and Arrangement of Functional Groups at the Carbon Sorbent Surface on Adsorption of Nitrogen-Containing Compounds

Investigation of the physical chemistry of adsorption by carbon sorbents is a very important task from both theoretical and practical points of view. The thermodynamics of adsorption is affected by defects on the sorbent surface, including oxygen-containing functional groups. In this work, adsorption of a series of aromatic and aliphatic amines and nitriles by the surface of graphitized thermal carbon black is simulated using the Monte Carlo method. The results of calculations are compared to the experimental data. The cases of isolated and paired arrangement of functional groups are considered, and regularities of interaction between different classes of nitrogen-containing compounds and such defects are revealed. On the basis of comparison with the experimental data, assumptions about the presence and distribution of hydroxyls on the surface of graphitized thermal carbon black are made.

Software defect prediction using over-sampling and feature extraction based on Mahalanobis distance

As the size of software projects becomes larger, software defect prediction (SDP) will play a key role in allocating testing resources reasonably, reducing testing costs, and speeding up the development process. Most SDP methods have used machine learning techniques based on common software metrics such as Halstead and McCabe’s cyclomatic. Datasets produced by these metrics usually do not follow Gaussian distribution, and also, they have overlaps in defect and non-defect classes. In addition, in many of software defect datasets, the number of defective modules (minority class) is considerably less than non-defective modules (majority class). In this situation, the performance of machine learning methods is reduced dramatically. Therefore, we first need to create a balance between minority and majority classes and then transfer the samples into a new space in which pair samples with same class (must-link set) are near to each other as close as possible and pair samples with different classes (cannot-link) stay as far as possible. To achieve the mentioned objectives, in this paper, Mahalanobis distance in two manners will be used. First, the minority class is oversampled based on the Mahalanobis distance such that generated synthetic data are more diverse from other minority data, and minority class distribution is not changed significantly. Second, a feature extraction method based on Mahalanobis distance metric learning is used which try to minimize distances of sample pairs in must-links and maximize the distance of sample pairs in cannot-links. To demonstrate the effectiveness of the proposed method, we performed some experiments on 12 publicly available datasets which are collected NASA repositories and compared its result by some powerful previous methods. The performance is evaluated in F-measure, G-Mean, and Matthews correlation coefficient. Generally, the proposed method has better performance as compared to the mentioned methods.

Hydrogen Arrangements on Defective Quasi-Molecular BN Fragments.

Considering the ever-increasing interest in metal-free materials, some potential chemical applications of quasi-molecular boron nitride (BN) derivatives were tested. Specifically, the behavior of BN fragments was analyzed when given defects, producing local electron density changes, were introduced by using topological engineering approaches. The inserted structural faults were Schottky-like divacancy (BN-d) defects, assembled in the fragment frame by the subtraction of one pair of B and N atoms or Stone–Wales (SW) defects. This study is aimed at highlighting the role of these important classes of defects in BN materials hypothesizing their future use in H2-based processes, related to either (i) H2 activation or (ii) H2 production, from preadsorbed hydrogenated molecular species on BN sites. Here, it has been observed that BN species, embodying SW defects, are characterized by endothermic H2 adsorption and fragmentation phenomena in order to guess their potential use in processes based on the transformation or production of hydrogen. On the contrary, in the presence of BN-d defects, and for reasons strictly related to local structural changes occurring along with the hydrogen rearrangements on the defective BN fragments, a possible use can be inferred. Precautions must be however taken to decrease the material rigidity that could actually decrease the ability of the BN fragment to flatten. This conversely seems to be a necessary requirement to have strong exothermic effects, following the rearrangements of the H2 molecules.