Nondestructive Evaluation of Modulus of Elasticity of Southern Pine LVL: Effect of Veneer Grade and Relative Humidity

Nondestructive testing (NDT) methods, stress-wave propagation, and transverse vibration were used to evaluate the modulus of elasticity (MOE) of laminated veneer lumber (LVL). Five types of LVL, fabricated with southern pine veneers of B. C, and D grades and liquid phenolic formaldehyde adhesive, were tested flatwise at environmental conditions of 65% and 95% relative humidity (RH) and 75°F (23.9°) to examine the influence of veneer grade and RH on some nondestructive mechanical properties of LVL. All LVLs, 1.5 in. (3.81 cm) thick X 3.5 in. (8.89 cm) high X 96 in. (243.84 cm) long, consisted of 13 plies of southern pine veneer, and their structural designs were: (I) all B grade veneers, (II) 2 plies of B grade veneer on both faces and all C grade veneers in the core plies, (III) 2 plies of B grade veneer on both faces and all D grade veneer in the core plies, (IV) all C grade veneers, and (V) all D grade veneers. Results indicated that MOE of LVL predicted by NDT was influenced by the veneer grade, and specimens fabricated with better grade veneers showed a higher value of MOE. A significant decrease in the MOE determined by both NDT methods was found when RH increased from 65% to 95% at 23.9° (75°F). The MOE measured by the stress-wave method was found to be more sensitive to the RH change than that determined by the transverse-vibration method. A lognormal distribution accurately described the distributions of MOEs determined by both nondestructive methods at both RH levels. As expected, a significant increase in moisture content (MC) in the LVL resulted from increasing RH levels. However, changes in densities of the tested materials due to the RH changes were found to be smaller. Results also indicated that regardless of the RH level. MOE determined from the stress-wave test was consistently higher than that obtained from the transverse-vibration test. For comparison. the results of tests on southern pine No. 1 and No. 2 grade lumber, commonly used in light-frame construction, are also presented. Analysis of the correlation between the static bending and NDT MOEs was made and results suggested that edgewise static bending MOE of LVL can be predicted with reasonable accuracy by the stress-wave testing. Good correlations were not observed between the edgewise static bending MOE and the nondestructive MOE evaluated by flatwise transverse vibration. However, excellent correlations between static bending and both NDT MOEs were observed in southern pine dimension lumber. Correlations between the MOEs evaluated by both nondestructive methods were found to be fair for LVL specimens

Regulation of APC/C-Cdh1 and Its Function in Neuronal Survival

This paper presents WebCQ, a prototype of a large-scale Web information monitoring system, WebCQ is designed to discover and detect changes to the World Wide Web (the Web) pages efficiently, and to notify users of interesting changes with a personalized customization. The system consists of four main components: a change detection robot that discovers and detects changes, a proxy cache service that reduces the communication traffics to the original information provider on the remote server, a tool that highlights changes between the web page last seen and the new version of the page, and a change notification service that delivers interesting changes and fresh information to the right users at the right time. A salient feature of our change detection robot is its ability to support various types of web page sentinels for finding and displaying interesting changes to web pages. This paper describes the WebCQ system with an emphasis on general issues in designing and engineering a la..

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201

LineMarkNet: Line Landmark Detection for Valet Parking

We aim for accurate and efficient line landmark detection for valet parking, which is a long-standing yet unsolved problem in autonomous driving. To this end, we present a deep line landmark detection system where we carefully design the modules to be lightweight. Specifically, we first empirically design four general line landmarks including three physical lines and one novel mental line. The four line landmarks are effective for valet parking. We then develop a deep network (LineMarkNet) to detect line landmarks from surround-view cameras where we, via the pre-calibrated homography, fuse context from four separate cameras into the unified bird-eye-view (BEV) space, specifically we fuse the surroundview features and BEV features, then employ the multi-task decoder to detect multiple line landmarks where we apply the center-based strategy for object detection task, and design our graph transformer to enhance the vision transformer with hierarchical level graph reasoning for semantic segmentation task. At last, we further parameterize the detected line landmarks (e.g., intercept-slope form) whereby a novel filtering backend incorporates temporal and multi-view consistency to achieve smooth and stable detection. Moreover, we annotate a large-scale dataset to validate our method. Experimental results show that our framework achieves the enhanced performance compared with several line detection methods and validate the multi-task network's efficiency about the real-time line landmark detection on the Qualcomm 820A platform while meantime keeps superior accuracy, with our deep line landmark detection system.Comment: 29 pages, 12 figure

Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance

ROC is usually used to analyze the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers potential optima for the given set of classifiers. Generally, ROC performance maximization could be considered to maximize the ROCCH, which also means to maximize the true positive rate (tpr) and minimize the false positive rate (fpr) for each classifier in the ROC space. However, tpr and fpr are conflicting with each other in the ROCCH optimization process. Though ROCCH maximization problem seems like a multi-objective optimization problem (MOP), the special characters make it different from traditional MOP. In this work, we will discuss the difference between them and propose convex hull-based multi-objective genetic programming (CH-MOGP) to solve ROCCH maximization problems. Convex hull-based sort is an indicator based selection scheme that aims to maximize the area under convex hull, which serves as a unary indicator for the performance of a set of points. A selection procedure is described that can be efficiently implemented and follows similar design principles than classical hyper-volume based optimization algorithms. It is hypothesized that by using a tailored indicator-based selection scheme CH-MOGP gets more efficient for ROC convex hull approximation than algorithms which compute all Pareto optimal points. To test our hypothesis we compare the new CH-MOGP to MOGP with classical selection schemes, including NSGA-II, MOEA/D) and SMS-EMOA. Meanwhile, CH-MOGP is also compared with traditional machine learning algorithms such as C4.5, Naive Bayes and Prie. Experimental results based on 22 well-known UCI data sets show that CH-MOGP outperforms significantly traditional EMOAs