Using patterns position distribution for software failure detection

Pattern-based software failure detection is an important topic of research in recent years. In this method, a set of patterns from program execution traces are extracted, and represented as features, while their occurrence frequencies are treated as the corresponding feature values. But this conventional method has its limitation due to ignore the pattern’s position information, which is important for the classification of program traces. Patterns occurs in the different positions of the trace are likely to represent different meanings. In this paper, we present a novel approach for using pattern’s position distribution as features to detect software failure. The comparative experiments in both artificial and real datasets show the effectiveness of this method

Using patterns position distribution for software failure detection

Pattern-based software failure detection is an important topic of research in recent years. In this method, a set of patterns from program execution traces are extracted, and represented as features, while their occurrence frequencies are treated as the corresponding feature values. But this conventional method has its limitation due to ignore the pattern’s position information, which is important for the classification of program traces. Patterns occurs in the different positions of the trace are likely to represent different meanings. In this paper, we present a novel approach for using pattern’s position distribution as features to detect software failure. The comparative experiments in both artificial and real datasets show the effectiveness of this method

Unravelling the functional biomechanics of dental features and tooth wear

Most of the morphological features recognized in hominin teeth, particularly the topography of the occlusal surface, are generally interpreted as an evolutionary functional adaptation for mechanical food processing. In this respect, we can also expect that the general architecture of a tooth reflects a response to withstand the high stresses produced during masticatory loadings. Here we use an engineering approach, finite element analysis (FEA), with an advanced loading concept derived from individual occlusal wear information to evaluate whether some dental traits usually found in hominin and extant great ape molars, such as the trigonid crest, the entoconid-hypoconulid crest and the protostylid have important biomechanical implications. For this purpose, FEA was applied to 3D digital models of three Gorilla gorilla lower second molars (M2) differing in wear stages. Our results show that in unworn and slightly worn M2s tensile stresses concentrate in the grooves of the occlusal surface. In such condition, the trigonid and the entoconid-hypoconulid crests act to reinforce the crown locally against stresses produced along the mesiodistal groove. Similarly, the protostylid is shaped like a buttress to suffer the high tensile stresses concentrated in the deep buccal groove. These dental traits are less functional in the worn M2, because tensile stresses decrease physiologically in the crown with progressing wear due to the enlargement of antagonistic contact areas and changes in loading direction from oblique to nearly parallel direction to the dental axis. This suggests that the wear process might have a crucial influence in the evolution and structural adaptation of molars enabling to endure bite stresses and reduce tooth failure throughout the lifetime of an individual

Development of a Computer Vision-Based Three-Dimensional Reconstruction Method for Volume-Change Measurement of Unsaturated Soils during Triaxial Testing

Problems associated with unsaturated soils are ubiquitous in the U.S., where expansive and collapsible soils are some of the most widely distributed and costly geologic hazards. Solving these widespread geohazards requires a fundamental understanding of the constitutive behavior of unsaturated soils. In the past six decades, the suction-controlled triaxial test has been established as a standard approach to characterizing constitutive behavior for unsaturated soils. However, this type of test requires costly test equipment and time-consuming testing processes. To overcome these limitations, a photogrammetry-based method has been developed recently to measure the global and localized volume-changes of unsaturated soils during triaxial test. However, this method relies on software to detect coded targets, which often requires tedious manual correction of incorrectly coded target detection information. To address the limitation of the photogrammetry-based method, this study developed a photogrammetric computer vision-based approach for automatic target recognition and 3D reconstruction for volume-changes measurement of unsaturated soils in triaxial tests. Deep learning method was used to improve the accuracy and efficiency of coded target recognition. A photogrammetric computer vision method and ray tracing technique were then developed and validated to reconstruct the three-dimensional models of soil specimen

A mask-based approach for the geometric calibration of thermal-infrared cameras

Accurate and efficient thermal-infrared (IR) camera calibration is important for advancing computer vision research within the thermal modality. This paper presents an approach for geometrically calibrating individual and multiple cameras in both the thermal and visible modalities. The proposed technique can be used to correct for lens distortion and to simultaneously reference both visible and thermal-IR cameras to a single coordinate frame. The most popular existing approach for the geometric calibration of thermal cameras uses a printed chessboard heated by a flood lamp and is comparatively inaccurate and difficult to execute. Additionally, software toolkits provided for calibration either are unsuitable for this task or require substantial manual intervention. A new geometric mask with high thermal contrast and not requiring a flood lamp is presented as an alternative calibration pattern. Calibration points on the pattern are then accurately located using a clustering-based algorithm which utilizes the maximally stable extremal region detector. This algorithm is integrated into an automatic end-to-end system for calibrating single or multiple cameras. The evaluation shows that using the proposed mask achieves a mean reprojection error up to 78% lower than that using a heated chessboard. The effectiveness of the approach is further demonstrated by using it to calibrate two multiple-camera multiple-modality setups. Source code and binaries for the developed software are provided on the project Web site