Investigation into Key Pavement Materials and Local Calibration on MEPDG

The release of Mechanistic-Empirical Pavement Design Guide (MEPDG) in 2004 has been leading a transition from empirically-based pavement design to a mechanical-empirical procedure. The pavement performance prediction models in the MEPDG combines design inputs such as material properties, traffic, and climate to the observed field performance. Since the prediction models were primarily calibrated through inputs and pavement performance data from Long Term Pavement Performance database, local calibrations were highly recommended due to the potential differences between national and local conditions. Key properties of pavement materials were investigated for the local calibration of the MEPDG, including the coefficient of thermal expansion (CTE) of cement concrete and the resilient modulus of soils. CTE values and other properties of concrete from eight concrete plants were investigated. A micromechanical model was proposed to predict concrete CTE considering the time and energy exhausted experimental methods. The thermal stress analysis was conducted on a composite material composed of aggregate and cement paste. Aggregate gradation was incorporated into the concrete CTE prediction model. The proposed model was validated by experimental data. Sensitivity analysis was also performed to explore the major factors affecting concrete CTE. The MEPDG utilizes the generalized model to describe the subgrade stiffness. Coefficients of the generalized model were regressed from the cyclic triaxial load test data for soils in Tennessee. Also the coefficients were correlated with soil physical properties and employed in evaluating the seasonal variation of subgrade resilient modulus. The influences of seasonal variation in subgrade resilient modulus on pavement performance were explored and found significant. Therefore, seasonal variation of soil resilient modulus should be considered in pavement design and analysis in MEPDG. The highway pavement sections in the Tennessee pavement management system were analyzed using the MEPDG version 1.1. This analysis indicates that the national calibrated models predict pavement performance poorly in comparison with measured data. Local calibrations on rutting transfer functions were conducted on the two main types of pavements, i.e., asphalt overlay on cement concrete pavement and asphalt overlay on asphalt pavement. With the local coefficients provided, the MEPDG provides better agreement between predicted and measured rutting

An Image Encryption Scheme Based on DNA Computing and Cellular Automata

Networks have developed very quickly, allowing the speedy transfer of image information through Internet. However, the openness of these networks poses a serious threat to the security of image information. The field of image encryption has drawn attention for this reason. In this paper, the concepts of 1-dimensional DNA cellular automata and T-DNA cellular automata are defined, and the concept of reversible T-DNA cellular automata is introduced. An efficient approach to encryption involving reversible T-DNA cellular automata as an encryption tool and natural DNA sequences as the main keys is here proposed. The results of a simulation experiment, performance analysis, and comparison to other encryption algorithms showed this algorithm to be capable of resisting brute force attacks, statistical attacks, and differential attacks. It also enlarged the key space enormously. It meets the criteria for one-time pad and resolves the problem that one-time pad is difficult to save

Facial Expression Recognition Based on SVM in E-learning

AbstractFacial expression is one of the most powerful channels of nonverbal communication which contains plenty of affective information. Recognition of facial expression and sending them back to the teacher is potentially helpful in E-learning. In this paper, we differentiate between person-relevant and person-irrelevant situations. Our goal is to extract powerful features used for facial expression recognition system in real-time and person-irrelevant situation. Previous work suggests that both facial shape features and appearance features could be used to recognize facial expressions. The first type is shape features calculated from positions on a face. The second type is a set of multi-scale and multi-orientation Gabor wavelet coefficients. The classifier is based on Support Vector Machines (SVM) and our expriments cover both person-relevant and person-irrelevant situations. The result shows that in person-irrelevant situation, using facial shape features outperforms using Gabor wavelet and it is faster. Furthermore, the radial basis function of SVM is more suitable for person-associated situation and the linear function describes person-irrelevant problems better

Calibration on MEPDG Low Temperature Cracking Model and Recommendation on Asphalt Pavement Structures in Seasonal Frozen Region of China

In order to implement the Mechanistic-Empirical Pavement Design Guide (MEPDG) to design and maintain asphalt pavements in China, it is necessary to calibrate transfer functions of distresses in MEPDG with local conditions, including traffics, environment, and materials as well as measured pavement distresses data in field. Comprehensive single factor sensitivity analyses of factors that influence thermal cracking of asphalt pavements were conducted utilizing the MEPDG low temperature cracking (LTC) model. Additionally, multiple factor sensitivity analyses were carried out as well, based on which pavement structures with sound thermal cracking resistance were recommended for seasonal frozen regions in China. Finally, the field data of thermal cracks on typical asphalt pavements in China was utilized to calibrate the LTC model in MEPDG. An improvement was proposed on MEPDG LTC model, after which was applied, the predicted thermal cracking from MEPDG LTC model agrees well with measured thermal cracking in China

Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-Sampling

Nowadays, gene chip technology has rapidly produced a wealth of information about gene expression activities. But the time-series expression data present a phenomenon that the number of genes is in thousands and the number of experimental data is only a few dozen. For such cases, it is difficult to learn network structure from such data. And the result is not ideal. So it needs to take measures to expand the capacity of the sample. In this paper, the Block bootstrap re-sampling method is utilized to enlarge the small expression data. At the same time, we apply “K2+T” algorithm to Yeast cell cycle gene expression data. Seeing from the experimental results and comparing with the semi-fixed structure EM learning algorithm, our proposed method is successful in constructing gene networks that capture much more known relationships as well as several unknown relationships which are likely to be novel

Solubilization of Hydroxypropyl-β-Cyclodextrin on Cholesterol in Aqueous Solution

Hydroxypropyl-β-Cyclodextrin (HP-β-CD), prepared via reaction of β-Cyclodextrin (β-CD) and propylene oxide (PO), is utilized to research solubilization of HP-β-CD on cholesterol in aqueous solution. HP-β-CD is characterized by Fourier Transform Infrared Spectrometry (FT-IR), and concentrations of cholesterol solution are measured by ultraviolet and visible (UV VIS) spectrophotometer. The research on optimal synthesis conditions of HP-β-CD indicates that sodium hydroxide amounts have the most effect on yields of product. The maximum solubilization multiples of HP-β-CD reaches 15, below which molecular rate of HP-β-CD and cholesterol in inclusion complex is 1:1

A Novel DWT-Based Watermarking for Image with The SIFT

A kind of scale invariant features transformation (SIFT for short) operators on DWT domain are proposed for watermarking algorithm. Firstly, the low frequency of the image is obtained by DWT. And then the SIFT transformation is used to calculate the key feature points for the low frequency sub-image. Based on the chosen space’s key points with moderate scale, a circular area as watermark embedding area is constructed. According to the research and final results, the novel digital watermark algorithm is proposed benefiting from the characteristics of SIFT’s key points and local time-frequency of DWT. The algorithm not only has good robustness to resist on such operations as compression, shearing, noise addition, median filtering and scaling, but also has good inhibition to possible watermark fake verification

P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation

Recently, Transformer-based models have achieved promising results in various vision tasks, due to their ability to model long-range dependencies. However, transformers are computationally expensive, which limits their applications in real-time tasks such as autonomous driving. In addition, an efficient local and global feature selection and fusion are vital for accurate dense prediction, especially driving scene understanding tasks. In this paper, we propose a real-time semantic segmentation architecture named Pyramid Pooling Axial Transformer (P2AT). The proposed P2AT takes a coarse feature from the CNN encoder to produce scale-aware contextual features, which are then combined with the multi-level feature aggregation scheme to produce enhanced contextual features. Specifically, we introduce a pyramid pooling axial transformer to capture intricate spatial and channel dependencies, leading to improved performance on semantic segmentation. Then, we design a Bidirectional Fusion module (BiF) to combine semantic information at different levels. Meanwhile, a Global Context Enhancer is introduced to compensate for the inadequacy of concatenating different semantic levels. Finally, a decoder block is proposed to help maintain a larger receptive field. We evaluate P2AT variants on three challenging scene-understanding datasets. In particular, our P2AT variants achieve state-of-art results on the Camvid dataset 80.5%, 81.0%, 81.1% for P2AT-S, P2ATM, and P2AT-L, respectively. Furthermore, our experiment on Cityscapes and Pascal VOC 2012 have demonstrated the efficiency of the proposed architecture, with results showing that P2AT-M, achieves 78.7% on Cityscapes. The source code will be available a

An integral gated mode single photon detector at telecom wavelengths

We demonstrate an integral gated mode single photon detector at telecom wavelengths. The charge number of an avalanche pulse rather than the peak current is monitored for single-photon detection. The transient spikes in conventional gated mode operation are canceled completely by integrating, which enables one to improve the performance of single photon detector greatly with the same avalanche photodiode. This method has achieved a detection efficiency of 29.9% at the dark count probability per gate equal to 5.57E-6/gate (1.11E-6/ns) at 1550nm.Comment: word to PDF, 3 pages with 4 figure