• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2377-2389
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• 2022

At present people have higher and higher requirements for network video quality, but video quality will be impaired by various factors, so video quality assessment has become more and more important. This paper focuses on the video quality assessment method using different fuzzy neural networks. Firstly, the main factors that impair the video quality are introduced, such as unit time jamming times, average pause time, blur degree and block effect. Secondly, two fuzzy neural network models are used to build the objective assessment method. By adjusting the network structure to optimize the assessment model, the objective assessment value of video quality is obtained. Meanwhile the advantages and disadvantages of the two models are analysed. Lastly, the proposed method is compared with many recent related assessment methods. This paper will give the experimental results and the detail of assessment process.

• Advances in concrete construction
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• 제15권6호
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• pp.391-404
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• 2023

In this study, the modeling of the transverse connection of fully precast steel-UHPC (Ultra-High-Performance Concrete) lightweight composite bridges were conducted. The transverse connection between precast components plays a critical role in the overall performance and safety of the bridge. To achieve an accurate and reliable simulation of the interface behavior, the cohesive model in ABAQUS was employed, considering both bending-tension and compression-shear behaviors. The parameters of the cohesive model are obtained through interface bending and oblique shear tests on UHPC samples with different surface roughness. By validating the numerical simulation against actual joint tests, the effectiveness and accuracy of the proposed model in capturing the interface behavior of the fully precast steel-UHPC lightweight composite bridge were demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2053-2067
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• 2023

This paper proposes two video quality assessment methods based on deep neural network. (i)The first method uses the IQF-CNN (convolution neural network based on image quality features) to build image quality assessment method. The LIVE image database is used to test this method, the experiment show that it is effective. Therefore, this method is extended to the video quality assessment. At first every image frame of video is predicted, next the relationship between different image frames are analyzed by the hysteresis function and different window function to improve the accuracy of video quality assessment. (ii)The second method proposes a video quality assessment method based on convolution neural network (CNN) and gated circular unit network (GRU). First, the spatial features of video frames are extracted using CNN network, next the temporal features of the video frame using GRU network. Finally the extracted temporal and spatial features are analyzed by full connection layer of CNN network to obtain the video quality assessment score. All the above proposed methods are verified on the video databases, and compared with other methods.

• Journal of Microbiology and Biotechnology
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• 제24권6호
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• pp.788-794
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• 2014

An extracellular ${\beta}$-glucosidase from Aspergillus niger Au0847 was purified to homogeneity by precipitation with ammonium sulfate, anion exchange, and gel filtration. The purified protein was composed of two subunits with molecular masses of 110 and 120 kDa. Au0847 ${\beta}$-glucosidase exhibited relatively high thermostability and pH stability, and its highest activity was obtained at $65^{\circ}C$ and pH 4.6, respectively. As a potential metalloprotein, its enzymatic activity was potently stimulated by manganese ion and DTT. The ${\beta}$-glucosidase displayed avid affinity and high catalytic efficiency for geniposide. Au0847 ${\beta}$-glucosidase has potential value as an industrial enzyme for the hydrolysis of geniposide to genipin.

• Advances in nano research
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• 제2권4호
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• pp.211-217
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• 2014

The self-assembled strain-induced sub-micrometric islands and nanostructures are grown from In-As-Sb-P quaternary liquid phase on InAs (100) substrates in Stranski-Krastanow growth mode. Two samples are under consideration. The first sample consists of unencapsulated islands and lens-shape quantum dots (QDs) grown from expressly inhomogeneous liquid phase. The second sample is an n-InAs/p-InAsSbP heterostructure with QDs embedded in the p-n junction interface. The morphology, size and shape of the structures are investigated by high-resolution scanning electron (SEM) and transmission electron (TEM) microscopy. It is shown that islands, as they decrease in size, undergo shape transitions. Particularly, as the volume decreases, the following succession of shape transitions are detected: sub-micrometric truncated pyramid, {111} facetted pyramid, {111} and partially {105} facetted pyramid, completely unfacetted "pre-pyramid", hemisphere, lens-shaped QD, which then evolves again to nano-pyramid. A critical size of $5{\pm}2nm$ for the shape transformation of InAsSbP-based lens-shaped QD to nano-pyramid is experimentally measured and theoretically evaluated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1785-1801
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• 2017

In recent years, image encryption algorithms have been developed rapidly in order to ensure the security of image transmission. With the assistance of our previous work, this paper proposes a novel chaotic image encryption algorithm based on self-adaptive model and feedback mechanism to enhance the security and improve the efficiency. Different from other existing methods where the permutation is performed by the self-adaptive model, the initial values of iteration are generated in a novel way to make the distribution of initial values more uniform. Unlike the other schemes which is on the strength of the feedback mechanism in the stage of diffusion, the piecewise linear chaotic map is first introduced to produce the intermediate values for the sake of resisting the differential attack. The security and efficiency analysis has been performed. We measure our scheme through comprehensive simulations, considering key sensitivity, key space, encryption speed, and resistance to common attacks, especially differential attack.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5782-5799
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• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

• Structural Monitoring and Maintenance
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• 제6권2호
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• pp.125-145
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• 2019

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

• Advances in nano research
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• 제13권3호
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• pp.297-312
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• 2022

Coughing and breath shortness are common symptoms of nano (small) cell lung cancer. Smoking is main factor in causing such cancers. The cancer cells form on the soft tissues of lung. Deformation behavior and wave vibration of lung affected when cancer cells exist. Therefore, in the current work, phase velocity behavior of the small cell lung cancer as a main part of the body via an exact size-dependent theory is presented. Regarding this problem, displacement fields of small cell lung cancer are obtained using first-order shear deformation theory with five parameters. Besides, the size-dependent small cell lung cancer is modeled via nonlocal stress/strain gradient theory (NSGT). An analytical method is applied for solving the governing equations of the small cell lung cancer structure. The novelty of the current study is the consideration of the five-parameter of displacement for curved panel, and porosity as well as NSGT are employed and solved using the analytical method. For more verification, the outcomes of this reports are compared with the predictions of deep neural network (DNN) with adaptive optimization method. A thorough parametric investigation is conducted on the effect of NSGT parameters, porosity and geometry on the phase velocity behavior of the small cell lung cancer structure.

• Smart Structures and Systems
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• 제29권1호
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.